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Found 332 papers, 124 papers with code
Generalization Bounds for Domain Adaptation
Afterwards, we analyze the asymptotic convergence and the rate of convergence of the learning process for such kind of domain adaptation.
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
For several rank minimization problems, such a replacement has been theoretically proven to be valid, i. e., the solution to nuclear norm minimization problem is also the solution to rank minimization problem.
Bennett-type Generalization Bounds: Large-deviation Case and Faster Rate of Convergence
samples, and then show that the generalization bounds have a faster rate of convergence than the traditional results.
Generalization Bounds for Representative Domain Adaptation
Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation.
A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm
A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields.
A Study on Cross-Population Age Estimation
Further, we study the amount of data needed in the target population to learn a cross-population age estimator.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
Relations among Some Low Rank Subspace Recovery Models
More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.
Unsupervised Feature Learning for Dense Correspondences across Scenes
We experimentally demonstrate that the learned features, together with our matching model, outperforms state-of-the-art methods such as the SIFT flow, coherency sensitive hashing and the recent deformable spatial pyramid matching methods both in terms of accuracy and computation efficiency.
Discrete Hyper-Graph Matching
Evaluations on both synthetic and real-world data corroborate the efficiency of our method.
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
As an application, we also find that the solutions to extended robust Low-Rank Representation and to our extended robust MC are mutually expressible, so both our theory and algorithm can be applied to the subspace clustering problem with missing values under certain conditions.
Shell PCA: Statistical Shape Modelling in Shell Space
In this paper we describe how to perform Principal Components Analysis in "shell space".
Engineering Deep Representations for Modeling Aesthetic Perception
To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting.
Functional Faces: Groupwise Dense Correspondence Using Functional Maps
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps.
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}.
Accelerated Variance Reduced Block Coordinate Descent
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space.
Hard-Aware Deeply Cascaded Embedding
This motivates us to ensemble a set of models with different complexities in cascaded manner and mine hard examples adaptively, a sample is judged by a series of models with increasing complexities and only updates models that consider the sample as a hard case.
Ranked #14 on
Image Retrieval
on SOP
Multiple-View Spectral Clustering for Group-wise Functional Community Detection
Functional connectivity analysis yields powerful insights into our understanding of the human brain.
Robust Spatial Filtering with Graph Convolutional Neural Networks
Graph-CNNs can handle both heterogeneous and homogeneous graph data, including graphs having entirely different vertex or edge sets.
Detailed, accurate, human shape estimation from clothed 3D scan sequences
We address the problem of estimating human pose and body shape from 3D scans over time.
Feature Incay for Representation Regularization
Thus, we propose feature incay to also regularize representation learning, which favors feature vectors with large norm when the samples can be correctly classified.
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.
RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ubiquitous applications.
AutoEncoder Inspired Unsupervised Feature Selection
High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty.
Binary output layer of feedforward neural networks for solving multi-class classification problems
Considered in this short note is the design of output layer nodes of feedforward neural networks for solving multi-class classification problems with r (bigger than or equal to 3) classes of samples.
The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients.
Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recognition.
High Order Recurrent Neural Networks for Acoustic Modelling
Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells.
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Finally we demonstrate the effectiveness of both approaches by visualizing the Class Activation Map (CAM) and discover that grid dropout is more aware of the whole facial areas and more robust than neuron dropout for small training dataset.
Semi-tied Units for Efficient Gating in LSTM and Highway Networks
Gating is a key technique used for integrating information from multiple sources by long short-term memory (LSTM) models and has recently also been applied to other models such as the highway network.
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Generating scene graph to describe all the relations inside an image gains increasing interests these years.
Ranked #1 on
Scene Graph Generation
on VRD
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics.
Weakly-Supervised Neural Text Classification
Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types.
OCNet: Object Context Network for Scene Parsing
To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.
Ranked #9 on
Semantic Segmentation
on Trans10K
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry
To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images.
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
To solve these problems, we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high resolution (HR) image with better textural details in the wavelet domain.
Sparse DNNs with Improved Adversarial Robustness
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications.
Generalization Bounds for Vicinal Risk Minimization Principle
First, we prove that the complexity of function classes convolving with vicinal functions can be controlled by that of the original function classes under the assumption that the function class is composed of Lipschitz-continuous functions.
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.
Ranked #4 on
Traffic Prediction
on SZ-Taxi
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data
In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convolutional neural network (CNN).
Audio Classification
Bird Species Classification With Audio-Visual Data
+3
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN
To convert the input into binary code, hashing algorithm has been widely used for approximate nearest neighbor search on large-scale image sets due to its computation and storage efficiency.
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion.
Databases
Weakly-Supervised Hierarchical Text Classification
During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism.
Attribute-Aware Attention Model for Fine-grained Representation Learning
Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner.
Ranked #4 on
Fine-Grained Image Classification
on CompCars
Speaker diarisation using 2D self-attentive combination of embeddings
This combination uses a 2-dimensional (2D) self-attentive structure, which extends the standard self-attentive layer by averaging not only across time but also across different types of embeddings.
Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square
We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely "combined Neyman-Pearson chi-square" ($\chi^2_{\mathrm{CNP}}$).
Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment
Blur Removal via Blurred-Noisy Image Pair
Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images.
A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation
We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity.
Task Oriented Channel State Information Quantization
The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter.
C3AE: Exploring the Limits of Compact Model for Age Estimation
Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.
Ranked #3 on
Age Estimation
on FGNET
FrameRank: A Text Processing Approach to Video Summarization
In constructing the dataset, because of the subjectivity of user-generated video summarization, we manually annotate 25 summaries for each video, which are in total 1300 summaries.
Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task.
Fragmentation of shells: An analogy with the crack formation in tree bark
We recognize that the fragmentation problem in shells is analogous to the cracking behavior of tree bark, and closed form solutions is obtained to describe the relationship between the expansion velocity and the number of necks with consideration of the strain rate dependent strength of the shell material.
Soft Condensed Matter Applied Physics
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs).
ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed.
Multi-Span Acoustic Modelling using Raw Waveform Signals
This paper proposes a novel multi-span structure for acoustic modelling based on the raw waveform with multiple streams of CNN input layers, each processing a different span of the raw waveform signal.
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction
In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.
Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment
We propose a novel multi-scale template matching method which is robust against both scaling and rotation in unconstrained environments.
Interlaced Sparse Self-Attention for Semantic Segmentation
There are two successive attention modules each estimating a sparse affinity matrix.
Orientation-aware Semantic Segmentation on Icosahedron Spheres
For the spherical domain, several methods recently adopt an icosahedron mesh, but systems are typically rotation invariant or require significant memory and parameters, thus enabling execution only at very low resolutions.
Ranked #21 on
Semantic Segmentation
on Stanford2D3D Panoramic
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
Integrating Source-channel and Attention-based Sequence-to-sequence Models for Speech Recognition
This paper proposes a novel automatic speech recognition (ASR) framework called Integrated Source-Channel and Attention (ISCA) that combines the advantages of traditional systems based on the noisy source-channel model (SC) and end-to-end style systems using attention-based sequence-to-sequence models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Decision Set Optimization and Energy-Efficient MIMO Communications
We propose a framework to find a good (finite) decision set which induces a minimal performance loss w. r. t.
Device-independent verification of Einstein-Podolsky-Rosen steering
We first establish the DI verification framework, relying on the measurement-device-independent technique and self-testing, and show it is able to verify all EPR-steerable states.
Quantum Physics
PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials.
Computational Physics Disordered Systems and Neural Networks Chemical Physics
On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications
We also develop the tail inequalities for matrix random series and matrix martingale difference sequence.
FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams
In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet.
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Taxonomies are of great value to many knowledge-rich applications.
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems.
Aggregated Gradient Langevin Dynamics
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.
Discriminative Neural Clustering for Speaker Diarisation
In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem.
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes.
Ranked #59 on
Person Re-Identification
on DukeMTMC-reID
Spherical Text Embedding
While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding.
Improved Large-margin Softmax Loss for Speaker Diarisation
Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers.
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications
This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.
G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data
The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image.
Self-Adaptive Training: beyond Empirical Risk Minimization
We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data.
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.
SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm
In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history.
paper2repo: GitHub Repository Recommendation for Academic Papers
Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.
Spherically symmetric static black holes in Einstein-aether theory
In particular, we find that, out of the five non-trivial field equations, only three are independent, so the problem is well-posed, as now generically there are only three unknown functions, {$F(r), B(r), A(r)$, where $F$ and $B$ are metric coefficients, and $A$ describes the aether field.}
General Relativity and Quantum Cosmology Astrophysics of Galaxies High Energy Physics - Phenomenology High Energy Physics - Theory
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them.
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
We study the open-domain named entity recognition (NER) problem under distant supervision.
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.
Ranked #1 on
Topic Models
on Arxiv HEP-TH citation graph
Neural Kalman Filtering for Speech Enhancement
Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A variational quantum algorithm for Hamiltonian diagonalization
To obtain the full spectrum of the Hamiltonian, we use a quantum imaginary time evolution algorithm with high temperature, which prepares a thermal state with a small correlation length.
Quantum Physics
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.
Example-based Color Transfer with Gaussian Mixture Modeling
In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids.
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem.
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.
A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling.
Transformer-Based Neural Text Generation with Syntactic Guidance
We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation.
Denoising Multi-Source Weak Supervision for Neural Text Classification
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources.
Text Classification Using Label Names Only: A Language Model Self-Training Approach
In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.
Ranked #1 on
Word Sense Disambiguation
on Words in Context
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction
We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres. We propose a reading comprehension framework for event extraction. Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem.
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.
Combination of Deep Speaker Embeddings for Diarisation
Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for clustering speech segments.
Emotion recognition by fusing time synchronous and time asynchronous representations
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis
Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech.
Deep Discriminative Feature Learning for Accent Recognition
Accent recognition with deep learning framework is a similar work to deep speaker identification, they're both expected to give the input speech an identifiable representation.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.
Theory-based Habit Modeling for Enhancing Behavior Prediction
Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment.
Hybrid Quantum-Classical Graph Convolutional Network
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data.
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning
We propose self-adaptive training -- a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost -- to advance both supervised and self-supervised learning of deep neural networks.
Multi-cell NOMA: Coherent Reconfigurable Intelligent Surfaces Model With Stochastic Geometry
Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.
Information Theory Information Theory
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions
Reinforcement learning (RL) agents in human-computer interactions applications require repeated user interactions before they can perform well.
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training
This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.
Ranked #1 on
3D Semantic Scene Completion
on NYUv2
A Riemannian smoothing steepest descent method for non-Lipschitz optimization on submanifolds
In this paper, we propose a Riemannian smoothing steepest descent method to minimize a nonconvex and non-Lipschitz function on submanifolds.
Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods
In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters, which can instantaneously provide the future values of the TBM load parameters as long as the current data are collected.
Analysis of the tradeoff between health and economic impacts of the Covid-19 epidemic
Should the measures be more (or less) restrictive?
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning
Extensive experiments show that our unsupervised learning method enables comparable detection performance to state-of-the-art supervised techniques, in both the intra- and inter-dataset settings.
Sketch-based Normal Map Generation with Geometric Sampling
This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (One-Shot)
on DuReader
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+19
Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model
Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks.
Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling
For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions.
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value.
Image Deformation Estimation via Multi-Objective Optimization
Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs).
Towards Transferable Adversarial Perturbations with Minimum Norm
In this work, we propose a \emph{geometry-aware framework} to generate transferable adversarial perturbation with minimum norm for each input.
Positive-Unlabeled Data Purification in the Wild for Object Detection
In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data.
GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins
Multi-fidelity data fusion (MDF) methods aims to use massive LF samples and small amounts of HF samples to develop an accurate and efficient model for describing the system with a reasonable computation burden.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition
Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-frame or per-label basis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Specifically, we train a tiny student model to match a pre-trained teacher model in the patch-level manifold space.
Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition
Furthermore, on the AMI corpus, the proposed conversion for language prior probabilities enables BERT to obtain an extra 3% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Auto-encoder based Model for High-dimensional Imbalanced Industrial Data
However, the successful applications of deep learning in soft sensing are still not widely integrated in factory control systems, because most of the research on soft sensing do not have access to large scale industrial data which are varied, noisy and incomplete.
Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition
Contextual knowledge is important for real-world automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
IEEE BigData 2021 Cup: Soft Sensing at Scale
The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques.
Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers
Here we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the Extended Kalman Filter algorithm (EKF), using the Behler-Parrinello neural network (BPNN) and two publicly accessible datasets of liquid water [Proc.
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.
Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks
Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input.
Self-Training with Differentiable Teacher
In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition
Our experiments show that for both losses, the WER on long-form speech reduces substantially as the training utterance length increases.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
HRFormer: High-Resolution Transformer for Dense Prediction
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Ranked #3 on
Pose Estimation
on AIC
Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas
Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections.
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning
Recovering binary programs' call graphs is crucial for inter-procedural analysis tasks and applications based on them. transfer One of the core challenges is recognizing targets of indirect calls (i. e., indirect callees).
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word
We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets.
Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer
Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing.
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder
In this paper, we present a deep generative model based method to generate diverse human motion interpolation results.
GraSSNet: Graph Soft Sensing Neural Networks
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.
Approximating Optimal Transport via Low-rank and Sparse Factorization
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned
To understand how the proposed model works, the deep visualization approach is applied.
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.
PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering
Subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques.
HRFormer: High-Resolution Vision Transformer for Dense Predict
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously.
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models
We propose {\ours}, a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning.
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning.
Cross-Impact of Order Flow Imbalance in Equity Markets
We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting.
Recurring the Transformer for Video Action Recognition
Besides, the proposed RViT can work on both fixed-length and variant-length video clips properly without requiring large GPU memory thanks to the frame by frame processing flow.
Towards Transferable Unrestricted Adversarial Examples with Minimum Changes
Extensive experiments verify the effectiveness of our framework on balancing imperceptibility and transferability of the crafted adversarial examples.
Improving the fusion of acoustic and text representations in RNN-T
The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.
Volatility forecasting with machine learning and intraday commonality
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility.
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using machine learning.
Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios.
Shift-Robust Node Classification via Graph Adversarial Clustering
Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.
Abandoning the Bayer-Filter to See in the Dark
Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome raw data.
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors
In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes.
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.
FORCE: A Framework of Rule-Based Conversational Recommender System
The conversational recommender systems (CRSs) have received extensive attention in recent years.
Learning a Structured Latent Space for Unsupervised Point Cloud Completion
Specifically, we map a series of related partial point clouds into multiple complete shape and occlusion code pairs and fuse the codes to obtain their representations in the unified latent space.
FlowFormer: A Transformer Architecture for Optical Flow
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
User sessions empower many search and recommendation tasks on a daily basis.
Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method
Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems.
Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator
MBWE and BLMD further improved the effectiveness of TCPGen and achieved more significant WER reductions on the biasing words.
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval
In global consistent quantization, we employ contrastive learning for both embedding and quantized representations and fuses these representations for consistent contrastive regularization between instances.
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition
Incorporating biasing words obtained as contextual knowledge is critical for many automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning Disentangled Representations for Controllable Human Motion Prediction
Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion.
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription
Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2. 0 (W2V2), have become the backbone of many speech tasks.
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
DETRs with Hybrid Matching
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections.
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.
Turn-Taking Prediction for Natural Conversational Speech
This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task.
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG
This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG).
Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification
Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network.
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach
Large Language Models have demonstrated remarkable few-shot performance, but the performance can be sensitive to the selection of few-shot instances.
A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing
The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature.
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation
The task is modeled by the minimization problem of a general goal function $f(x;g)$ for which the decision $x$ has to be taken from a quantized version of the parameters $g$.
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
Multi-Objective Personalized Product Retrieval in Taobao Search
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition
In this paper, we make use of limited code-switching data as driving materials and explore a shortcut to quickly develop intra-sentential code-switching recognition skill on the commissioned native language acoustic model, where we propose a data-driven method to make the seed lexicon which is used to train grapheme-to-phoneme model to predict mapping pronunciations for foreign language word in code-switching sentences.
