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Found 436 papers, 124 papers with code
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].
Ranked #1 on
Node Classification
on Amazon2M
Mapping Natural Language Instructions to Mobile UI Action Sequences
We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it.
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work.
Enabling Conversational Interaction with Mobile UI using Large Language Models
This paper investigates the feasibility of enabling versatile conversational interactions with mobile UIs using a single LLM.
Automatic Macro Mining from Interaction Traces at Scale
Macros are building block tasks of our everyday smartphone activity (e. g., "login", or "booking a flight").
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.
Ranked #81 on
Code Generation
on MBPP
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Robust Estimation of Similarity Transformation for Visual Object Tracking
In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result.
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
Ranked #5 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2).
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.
4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface
Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics.
Graph-based Topology Reasoning for Driving Scenes
Understanding the road genome is essential to realize autonomous driving.
Ranked #5 on
3D Lane Detection
on OpenLane-V2 val
Lepard: Learning partial point cloud matching in rigid and deformable scenes
We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes.
Ranked #1 on
Partial Point Cloud Matching
on 4DMatch
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
We verify the effectiveness of the proposed explanations from two aspects: (1) Counterfactual Trace Visualization: the enhanced forgery images are useful to reveal artifacts by visually contrasting the original images and two different visualization methods; (2) Transferable Adversarial Attacks: the adversarial forgery images generated by attacking the detection model are able to mislead other detection models, implying the removed artifacts are general.
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
Non-rigid point cloud registration is a key component in many computer vision and computer graphics applications.
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.
PaLI-X: On Scaling up a Multilingual Vision and Language Model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.
Ranked #1 on
Fine-Grained Image Recognition
on OVEN
Robustness Verification of Tree-based Models
We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles, the verification problem can be cast as a max-clique problem on a multi-partite graph with bounded boxicity.
INT2: Interactive Trajectory Prediction at Intersections
One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
Graph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based applications.
Ranked #11 on
Node Property Prediction
on ogbn-mag
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
Homography Decomposition Networks for Planar Object Tracking
Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.
MSPKmerCounter: A Fast and Memory Efficient Approach for K-mer Counting
For the task of k-mer counting, MSPKmerCounter offers a very fast and memory-efficient solution.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Efficient End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
Improving Deep Neural Network with Multiple Parametric Exponential Linear Units
In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
Distance-Aware Occlusion Detection with Focused Attention
In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection.
Human-Object Interaction Detection
Relationship Detection
+1
Multi-Modal Automatic Prosody Annotation with Contrastive Pretraining of SSWP
While human prosody annotation contributes a lot to the performance, it is a labor-intensive and time-consuming process, often resulting in inconsistent outcomes.
Understanding Embodied Reference with Touch-Line Transformer
Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.
Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion
Objective and subjective evaluations show that \textit{Phoneme Hallucinator} outperforms existing VC methods for both intelligibility and speaker similarity.
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale
The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen.
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
Attentional mechanisms are order-invariant.
Attribute-aware Pedestrian Detection in a Crowd
To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Cooperative Open-ended Learning Framework for Zero-shot Coordination
However, these approaches can result in a loss of learning and an inability to cooperate with certain strategies within the population, known as cooperative incompatibility.
Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy.
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech
Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems.
An Information-Theoretic Metric of Transferability for Task Transfer Learning
An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.
Rethinking Training from Scratch for Object Detection
The ImageNet pre-training initialization is the de-facto standard for object detection.
Node Dependent Local Smoothing for Scalable Graph Learning
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Proximal Policy Optimization with Mixed Distributed Training
Instability and slowness are two main problems in deep reinforcement learning.
Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers.
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
On top of the observations that industrial formulae exhibit clear community structure and oversplit substructures lead to the difficulty in semantic formation of logical structures, we propose HardSATGEN, which introduces a fine-grained control mechanism to the neural split-merge paradigm for SAT formula generation to better recover the structural and computational properties of the industrial benchmarks.
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations
Specifically, we use optimal transport to estimate domain difference and the optimal coupling between source and target distributions, which is then used to derive the conditional entropy of the target task (task difference).
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor.
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain.
$L_0$-ARM: Network Sparsification via Stochastic Binary Optimization
Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training.
Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormers
To further improve the model performance, multi-model ensemble and threshold optimization were used to produce the final probabilistic rain prediction.
NRTSI: Non-Recurrent Time Series Imputation
Time series imputation is a fundamental task for understanding time series with missing data.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
Chinese Idiom Paraphrasing
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
Flow Models for Arbitrary Conditional Likelihoods
Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks.
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$.
Multitask Learning for Emotion and Personality Detection
In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual.
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other.
Area Attention
We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e. g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences.
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.
Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection
We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
FusionINN: Invertible Image Fusion for Brain Tumor Monitoring
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image.
Joint Optimization for SSIM-Based CTU-Level Bit Allocation and Rate Distortion Optimization
However, the MSE-based rate distortion optimization (RDO) was still used in these studies.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
In this paper, we have constructed a consistent formal plane geometry system.
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading.
Meta-Neighborhoods
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence.
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks.
FGeo-HyperGNet: Geometric Problem Solving Integrating Formal Symbolic System and Hypergraph Neural Network
The symbolic part is a formal system built on FormalGeo, which can automatically perform geomertic relational reasoning and algebraic calculations and organize the solving process into a solution hypertree with conditions as hypernodes and theorems as hyperedges.
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
In this paper, we introduce ChunkAttention, a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime to improve the memory utilization of KV cache.
Saliency detection based on structural dissimilarity induced by image quality assessment model
Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features.
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks.
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
However, it remains challenging to model the negation and union operator.
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data.
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields.
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities.
Exchangeable Generative Models with Flow Scans
In this work, we develop a new approach to generative density estimation for exchangeable, non-i. i. d.
Enhance Multimodal Model Performance with Data Augmentation: Facebook Hateful Meme Challenge Solution
In this paper, we utilize multi-modal, pre-trained models VilBERT and Visual BERT.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level.
FewSAR: A Few-shot SAR Image Classification Benchmark
Motivated by this observation, we propose a novel few-shot SAR image classification benchmark (FewSAR) to address this issue.
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
This results in a set of candidate answers.
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Instead, we propose two methods to improve the representational power of AGCs by utilizing 1) structural information in a high-dimensional space and 2) multiple attention functions when calculating their weights.
Learning a Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning
This approach optimizes network parameters in the null space of the past tasks' feature representation matrix to guarantee the stability.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution.
PagPassGPT: Pattern Guided Password Guessing via Generative Pretrained Transformer
Amidst the surge in deep learning-based password guessing models, challenges of generating high-quality passwords and reducing duplicate passwords persist.
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.
RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US
Climate change has largely impacted our daily lives.
ROD: Reception-aware Online Distillation for Sparse Graphs
Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback.
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions.
GloTSFormer: Global Video Text Spotting Transformer
In this paper, we propose a novel Global Video Text Spotting Transformer GloTSFormer to model the tracking problem as global associations and utilize the Gaussian Wasserstein distance to guide the morphological correlation between frames.
Neural Plasticity Networks
To the best of our knowledge, this is the first learning framework that unifies network sparsification and network expansion in an end-to-end training pipeline.
Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas
In this work, we propose a data-driven approach to extract stochastic governing laws with both (Gaussian) Brownian motion and (non-Gaussian) L\'evy motion, from short bursts of simulation data.
Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Graph neural networks (GNNs) have been intensively applied to various graph-based applications.
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals.
Kernel Learning in Ridge Regression "Automatically" Yields Exact Low Rank Solution
Assuming that the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), we find that the global minimizer of the finite sample kernel learning objective is also low rank with high probability.
Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation
While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles.
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings.
Progressive Neural Networks for Image Classification
The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images.
Tensor graph convolutional neural network
For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs.
PotentialNet for Molecular Property Prediction
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.
Nonlinear Online Learning with Adaptive Nyström Approximation
Use of nonlinear feature maps via kernel approximation has led to success in many online learning tasks.
Time-Dependent Representation for Neural Event Sequence Prediction
Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
Disentangled Variational Auto-Encoder for Semi-supervised Learning
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA
Due to its efficiency and stability, Robust Principal Component Analysis (RPCA) has been emerging as a promising tool for moving object detection.
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer.
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet.
Recurrent Regression for Face Recognition
To address the sequential changes of images including poses, in this paper we propose a recurrent regression neural network(RRNN) framework to unify two classic tasks of cross-pose face recognition on still images and video-based face recognition.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
Gland Instance Segmentation by Deep Multichannel Side Supervision
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
And the dynamic features are formed by using the information of its neighbor frames in the sequence.
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses.
Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis
Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes.
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
The algorithm achieves an order of magnitude faster inference than the original softmax layer for predicting top-$k$ words in various tasks such as beam search in machine translation or next words prediction.
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates
The performance of deep neural networks crucially depends on good hyperparameter configurations.
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information.
Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN
In this way, the advantages of multiple models can be combined to mention the recognition accuracy.
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Guess Me if You Can: Acronym Disambiguation for Enterprises
The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output.
Hashtag Recommendation with Topical Attention-Based LSTM
Microblogging services allow users to create hashtags to categorize their posts.
Ensemble Feature for Person Re-Identification
However, deep feature extracted by a single CNN network is not robust enough in testing stage.
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.
Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches
Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization.
3D Model-Based Continuous Emotion Recognition
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications.
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries
This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view.
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and autonomous systems.
A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors).
The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality
A family of Markov blankets in a faithful Bayesian network satisfies the symmetry and consistency properties.
Proving the NP-completeness of optimal moral graph triangulation
Moral graphs were introduced in the 1980s as an intermediate step when transforming a Bayesian network to a junction tree, on which exact belief propagation can be efficiently done.
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
In this paper, we propose to leverage graph optimization and loop closure detection to overcome limitations of unsupervised learning based monocular visual odometry.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s).
Integrating Tensor Similarity to Enhance Clustering Performance
The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix.
Event Detection without Triggers
The goal of event detection (ED) is to detect the occurrences of events and categorize them.
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length
This paper addresses the issue and proposes a criterion for linear dynamical system based on the principle of minimum description length.
Bayesian Optimization with Directionally Constrained Search
This constraint demands an optimizer to be aware of its remaining budget and able to spend it wisely, in order to return as better a point as possible.
Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets
In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets.
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning
We propose a hybrid hardware-software framework that has the potential to significantly reduce the computational complexity and memory requirements of on-device machine learning.
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations.
SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics
In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control.
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL).
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders
While we focus on interface layout prediction, our model can be generally applicable for other layout prediction problems that involve tree structures and 2-dimensional placements.
Illumination adaptive person reid based on teacher-student model and adversarial training
Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation.
Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head
To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios.
Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization
Versions of the ten DE algorithms based on individuals redistribution are compared with not only original version but also version based on complete restart, where individuals redistribution and complete restart are based on the same entry criterion.
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation.
Defense Through Diverse Directions
In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training.
Learning to Optimize Non-Rigid Tracking
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop.
SSIM-Based CTU-Level Joint Optimal Bit Allocation and Rate Distortion Optimization
This problem is due to the lack of an accurate R-$D_\text{SSIM}$ model that can be used uniformly in both OBA and RDO.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
We describe the annotation process in detail and compare it with other similar evaluation systems.
A Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noise
We then design a numerical algorithm to compute the drift, diffusion coefficient and jump measure, and thus extract a governing stochastic differential equation with Gaussian and non-Gaussian noise.
A heterogeneous branch and multi-level classification network for person re-identification
A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block.
Grafted network for person re-identification
In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion.
Dynamic Feature Acquisition with Arbitrary Conditional Flows
To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire.
Probabilistic Classification Vector Machine for Multi-Class Classification
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems.
SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes
We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene.
RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model
To solve this challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different domains and guide the ReID backbone to learn better ReID information.
Multi-Stage Influence Function
With this score, we can identify the pretraining examples in the pretraining task that contribute most to a prediction in the finetuning task.
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement.
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases.
Exchangeable Neural ODE for Set Modeling
Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements.
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation
To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model.
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition
So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples.
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention
The success of Deep Neural Networks (DNNs) highly depends on data quality.
InvertGAN: Reducing mode collapse with multi-dimensional Gaussian Inversion
Generative adversarial networks have shown their ability in capturing high-dimensional complex distributions and generating realistic data samples e. g. images.
Active Feature Acquisition with Generative Surrogate Models
Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data.
A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systems
The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems.
Anomalous Motion of Particle Levitated by Laguerre-Gaussian beam
We calculated the scattering force distribution of a micro-particle trapped in an optical tweezers formed by the strongly focused LG beam, and showed that there exist stable trajectories of the particle that controlled by the negative torque.
Optics
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Detecting public sentiment drift is a challenging task due to sentiment change over time.
Interpretable Models in ANNs
Artificial neural networks are often very complex and too deep for a human to understand.
Efficient Automatic CASH via Rising Bandits
In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods.
Deep Learning for Short-Term Voltage Stability Assessment of Power Systems
Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics.
Partially Observed Exchangeable Modeling
Modeling dependencies among features is fundamental for many machine learning tasks.
