Search Results for author:
Found 130 papers, 53 papers with code
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning
Traditional fair spectral clustering (FSC) methods consist of two consecutive stages, i. e., performing fair spectral embedding on a given graph and conducting $k$means to obtain discrete cluster labels.
On the Opportunities of Green Computing: A Survey
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
DyExplainer: Explainable Dynamic Graph Neural Networks
Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships.
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets.
Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond
Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.
Learning Graph Filters for Spectral GNNs via Newton Interpolation
Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.
Semi-Supervised End-To-End Contrastive Learning For Time Series Classification
The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier.
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills.
Certifiably Robust Graph Contrastive Learning
Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model.
Dynamic DAG Discovery for Interpretable Imitation Learning
After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.
CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving
Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains.
Generalizing Event-Based Motion Deblurring in Real-World Scenarios
Event-based motion deblurring has shown promising results by exploiting low-latency events.
Random Walk on Multiple Networks
To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
Despite these developments, the task of accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains challenging due to the insufficient discriminability of ReID features and the predominant use of linear motion models in MOT.
Ranked #10 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
ReSup: Reliable Label Noise Suppression for Facial Expression Recognition
To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression.
Facial Expression Recognition
Facial Expression Recognition (FER)
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data.
Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs
In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
Power Grid Transient Analysis via Open-Source Circuit Simulator: A Case Study of HVDC
This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused.
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss.
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events
Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to the missing temporal dynamic information in both RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions is unavailable.
Recovering Continuous Scene Dynamics from A Single Blurry Image with Events
This paper aims at demystifying a single motion-blurred image with events and revealing temporally continuous scene dynamics encrypted behind motion blurs.
Enhancing Clinical Evidence Recommendation with Multi-Channel Heterogeneous Learning on Evidence Graphs
Clinical evidence encompasses the associations and impacts between patients, interventions (such as drugs or physiotherapy), problems, and outcomes.
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets.
Time Series Contrastive Learning with Information-Aware Augmentations
A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.
Learning to Super-Resolve Blurry Images with Events
Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution.
Unnoticeable Backdoor Attacks on Graph Neural Networks
In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph.
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image.
Ranked #5 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRCO
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.
Graph Topology Learning Under Privacy Constraints
Towards this end, we propose a framework where personalized graphs for local clients as well as a consensus graph are jointly learned.
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.
Uni-3D: A Universal Model for Panoptic 3D Scene Reconstruction
Performing holistic 3D scene understanding from a single-view observation, involving generating instance shapes and 3D scene segmentation, is a long-standing challenge.
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity
By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network.
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning
Diverse visual stimuli can evoke various human affective states, which are usually manifested in an individual's muscular actions and facial expressions.
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation.
Learning to See Through with Events
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions.
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
Personalized Federated Learning via Heterogeneous Modular Networks
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Graph neural networks (GNNs) have achieved great success in various graph problems.
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection.
Omni-directional Pathloss Measurement Based on Virtual Antenna Array with Directional Antennas
Omni-directional pathloss, which refers to the pathloss when omni-directional antennas are used at the link ends, are essential for system design and evaluation.
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.
EGCN: An Ensemble-based Learning Framework for Exploring Effective Skeleton-based Rehabilitation Exercise Assessment
We also examine the properness of existing evaluation criteria and focus on evaluating the prediction ability of our proposed method.
Ranked #1 on
Action Assessment
on KIMORE
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
In many real-world scenarios, node classes are imbalanced, with some majority classes making up most parts of the graph.
Prefix Conditioning Unifies Language and Label Supervision
In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation.
Towards Faithful and Consistent Explanations for Graph Neural Networks
Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input.
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos
Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR.
Ranked #1 on
Action Recognition In Videos
on PKU-MMD
(using extra training data)
Learning to Purification for Unsupervised Person Re-identification
To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme.
Knowledge Distillation
Unsupervised Person Re-Identification
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.
Text Spotting Transformers
In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild.
Ranked #6 on
Text Spotting
on ICDAR 2015
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels
Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations.
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes.
Unifying Motion Deblurring and Frame Interpolation with Events
Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos.
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers
In this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT) method for AU detection, which learns AU encoding features representation from different modalities by transformer encoder and fuses modalities by another fusion transformer module.
Exploring Edge Disentanglement for Node Classification
Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement.
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Our framework AutoDistil addresses above challenges with the following steps: (a) Incorporates inductive bias and heuristics to partition Transformer search space into K compact sub-spaces (K=3 for typical student sizes of base, small and tiny); (b) Trains one SuperLM for each sub-space using task-agnostic objective (e. g., self-attention distillation) with weight-sharing of students; (c) Lightweight search for the optimal student without re-training.
Synthetic Aperture Imaging With Events and Frames
This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions.
InfoGCL: Information-Aware Graph Contrastive Learning
The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized.
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks
The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee.
Online Graph Learning in Dynamic Environments
Inferring the underlying graph topology that characterizes structured data is pivotal to many graph-based models when pre-defined graphs are not available.
Time-varying Graph Learning Under Structured Temporal Priors
Different from many existing chain structure based methods in which the priors like temporal homogeneity can only describe the variations of two consecutive graphs, we propose a structure named \emph{temporal graph} to characterize the underlying real temporal relations.
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data.
Information-Aware Time Series Meta-Contrastive Learning
How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
Most of the existing contrastive learning methods employ pre-defined view generation methods, e. g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well.
ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks
Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning.
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface
In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.
Using Terminal Circuit for Power System Electromagnetic Transient Simulation
The modern power system is evolving with increasing penetration of power electronics introducing complicated electromagnetic phenomenon.
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings.
Controlling Neural Networks with Rule Representations
The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.
Power System Transient Modeling and Simulation using Integrated Circuit
Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system.
Robust Graph Learning Under Wasserstein Uncertainty
To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.
BM-NAS: Bilevel Multimodal Neural Architecture Search
Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems.
Pose Recognition with Cascade Transformers
Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.
Semi-Supervised Graph-to-Graph Translation
For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.
Event-based Synthetic Aperture Imaging with a Hybrid Network
Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images.
Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing
Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols.
Information Theory Networking and Internet Architecture Information Theory
A New Design of Cache-aided Multiuser Private Information Retrieval with Uncoded Prefetching
This paper considers the MuPIR problem with two messages, arbitrary number of users and databases where uncoded prefetching is assumed, i. e., the users directly copy some bits from the library as their cached contents.
A Two-stream Neural Network for Pose-based Hand Gesture Recognition
The self-attention based graph convolutional network has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCN.
DyHCN: Dynamic Hypergraph Convolutional Networks
In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations.
Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy
In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way.
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics.
Parameterized Explainer for Graph Neural Network
The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings.
On the Fundamental Limits of Cache-aided Multiuser Private Information Retrieval
Based on the proposed novel approach of \emph{cache-aided interference alignment (CIA)}, first, for the MuPIR problem with $K=2$ messages, $K_{\rm u}=2$ users and $N\ge 2$ databases, we propose achievable retrieval schemes for both uncoded and general cache placement.
Improving Spiking Sparse Recovery via Non-Convex Penalties
Compared with digital methods, sparse recovery based on spiking neural networks has great advantages like high computational efficiency and low power-consumption.
Automatic Image Labelling at Pixel Level
A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain.
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Here, we develop GNNGuard, a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure.
Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN
Conclusions: Multiple channels and knowledge embeddings enhance the performance of the CNN model in the task of biomedical literature triage.
ProbaNet: Proposal-balanced Network for Object Detection
Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall performance.
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
The major task of PICO extraction is to extract sentences from medical literature and classify them into each class.
Adversarial Imitation Attack
Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.
Entity Profiling in Knowledge Graphs
Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs.
A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).
Residual-Guided In-Loop Filter Using Convolution Neural Network
The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc.
BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis
The ever evolving informatics technology has gradually bounded human and computer in a compact way.
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing
To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.
Adversarial Variational Embedding for Robust Semi-supervised Learning
However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
An ATNNFAE consists of an auto-encoder where the internal code is normalized on the unit sphere and corrupted by additive noise.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Multi-modality Sensor Data Classification with Selective Attention
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification
A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification.
Metric Factorization: Recommendation beyond Matrix Factorization
In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.
Byte-Level Recursive Convolutional Auto-Encoder for Text
The proposed model is a multi-stage deep convolutional encoder-decoder framework using residual connections, containing up to 160 parameterized layers.
MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network
The proposed approach is evaluated over 3 datasets (two local and one public).
Human-Computer Interaction
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.
Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.
Human-Computer Interaction Neurons and Cognition
Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?
This article offers an empirical study on the different ways of encoding Chinese, Japanese, Korean (CJK) and English languages for text classification.
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.
Automatically Labeled Data Generation for Large Scale Event Extraction
Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data.
DeepKey: An EEG and Gait Based Dual-Authentication System
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
A long-term goal of machine learning is to build intelligent conversational agents.
Universum Prescription: Regularization using Unlabeled Data
This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning.
Ranked #160 on
Image Classification
on CIFAR-10
Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
Ranked #16 on
Sentiment Analysis
on Yelp Fine-grained classification
Text Understanding from Scratch
This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets).
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks.
Flexible and Robust Co-Regularized Multi-Domain Graph Clustering.
Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge.
