Search Results for author:
Found 332 papers, 124 papers with code
Interlaced Sparse Self-Attention for Semantic Segmentation
There are two successive attention modules each estimating a sparse affinity matrix.
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
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
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.
SALMONN: Towards Generic Hearing Abilities for Large Language Models
Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory information consisting of at least three types of sounds: speech, audio events, and music.
APISR: Anime Production Inspired Real-World Anime Super-Resolution
In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts.
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
In this paper, we reconsider speculative sampling and derive two key observations.
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
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.
Large Language Models for Generative Information Extraction: A Survey
Information extraction (IE) aims to extract structural knowledge (such as entities, relations, and events) from plain natural language texts.
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
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.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
We study the open-domain named entity recognition (NER) problem under distant supervision.
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.
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
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
ToolQA: A Dataset for LLM Question Answering with External Tools
To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering.
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
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.
A Survey on Programmatic Weak Supervision
Labeling training data has become one of the major roadblocks to using machine learning.
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
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.
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.
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks.
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.
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
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.
TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots.
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
C3: Zero-shot Text-to-SQL with ChatGPT
This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82. 3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge.
Ranked #4 on
Text-To-SQL
on spider
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.
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.
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
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.
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
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.
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.
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.
AdaPlanner: Adaptive Planning from Feedback with Language Models
We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback.
Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation
Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training.
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.
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
RAIN: Your Language Models Can Align Themselves without Finetuning
We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting.
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
Can Contextual Biasing Remain Effective with Whisper and GPT-2?
End-to-end automatic speech recognition (ASR) and large language models, such as Whisper and GPT-2, have recently been scaled to use vast amounts of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
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.
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.
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.
COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning
We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the generalization ability of dense retrieval by combating the distribution shifts between source training tasks and target scenarios.
Ranked #1 on
Zero-shot Text Search
on CQADupStack
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.
How Far Have We Gone in Vulnerability Detection Using Large Language Models
Given the significant successes of large language models (LLMs) in various tasks, there is growing anticipation of their efficacy in vulnerability detection.
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.
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.
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.
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).
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.
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.
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.
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
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.
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
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.
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.
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.
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
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.
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.
Distribution-based Emotion Recognition in Conversation
Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence.
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding
The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged.
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.
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks.
Ranked #1 on
Zero-Shot Text Classification
on AG News
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.
kTrans: Knowledge-Aware Transformer for Binary Code Embedding
By feeding explicit knowledge as additional inputs to the Transformer, and fusing implicit knowledge with a novel pre-training task, kTrans provides a new perspective to incorporating domain knowledge into a Transformer framework.
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
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.
End-to-End Stochastic Optimization with Energy-Based Model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
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.
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.
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models
We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training.
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.
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
Neighborhood-Regularized Self-Training for Learning with Few Labels
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.
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.
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.
Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models
Audio-visual large language models (LLM) have drawn significant attention, yet the fine-grained combination of both input streams is rather under-explored, which is challenging but necessary for LLMs to understand general video inputs.
PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever.
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.
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling
Most existing methods for learning from noisy labels use static input features for denoising, but these methods are limited by the information they can provide on true label distributions and can result in biased or incorrect predictions.
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression
In automatic emotion recognition (AER), labels assigned by different human annotators to the same utterance are often inconsistent due to the inherent complexity of emotion and the subjectivity of perception.
Autoregressive Diffusion Model for Graph Generation
However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
BBox-Adapter distinguishes target and source domain data by treating target data as positive and source data as negative.
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.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
Two metrics are proposed to evaluate AER performance with automatic segmentation based on time-weighted emotion and speaker classification errors.
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor.
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains.
End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator
Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model component, is studied, which leverages a slot shortlist with corresponding entities to extract biasing lists.
Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator
The incorporation of biasing words obtained through contextual knowledge is of paramount importance in automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
MUBen: Benchmarking the Uncertainty of Molecular Representation Models
While some studies have included UQ to improve molecular pre-trained models, the process of selecting suitable backbone and UQ methods for reliable molecular uncertainty estimation remains underexplored.
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER).
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
We study the problem of extracting N-ary relation tuples from scientific articles.
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.
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.
Enhancing Quantised End-to-End ASR Models via Personalisation
Recent end-to-end automatic speech recognition (ASR) models have become increasingly larger, making them particularly challenging to be deployed on resource-constrained devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Study
Large Language Models (LLMs) have shown remarkable proficiency in language understanding and have been successfully applied to a variety of real-world tasks through task-specific fine-tuning or prompt engineering.
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses.
Direct-Effect Risk Minimization for Domain Generalization
In this work, we introduce the concepts of direct and indirect effects from causal inference to the domain generalization problem.
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.
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment.
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing
We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies.
A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference
In this paper, we present a solution for enhancing video alignment to improve multi-step inference.
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
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.
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.
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}.
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.
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.
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.
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.
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.
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.
Multiple-View Spectral Clustering for Group-wise Functional Community Detection
Functional connectivity analysis yields powerful insights into our understanding of the human brain.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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
Transferring SLU Models in Novel Domains
When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.
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.
Discrete Hyper-Graph Matching
Evaluations on both synthetic and real-world data corroborate the efficiency of our method.
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.
Shell PCA: Statistical Shape Modelling in Shell Space
In this paper we describe how to perform Principal Components Analysis in "shell space".
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.
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.
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.
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.
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-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.
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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.
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
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.
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.
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
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.
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
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
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
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.
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
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.
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
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.
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
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.
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?
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.
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.
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.
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.
