Search Results for author:
Found 158 papers, 81 papers with code
VoxEval: Benchmarking the Knowledge Understanding Capabilities of End-to-End Spoken Language Models
With the growing demand for developing speech-based interaction models, end-to-end Spoken Language Models (SLMs) have emerged as a promising solution.
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations.
Data Augmentation Techniques for Chinese Disease Name Normalization
Disease name normalization is an important task in the medical domain.
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advancements across domains such as natural language processing, computer vision, and scientific discovery.
NILE: Internal Consistency Alignment in Large Language Models
However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT.
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
Firstly, the Retrieval module gathers supporting triples from the KG, collects plausible candidate answers from a base embedding model, and retrieves context for each related entity.
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs
This work advances parameter-efficient fine-tuning for LLMs, and offers a promising solution for adapting LLMs to downstream tasks while optimizing performance and efficiency.
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
With sufficient guidance from proper prompts and supervised fine-tuning, CATS activates the strong semantic understanding and reasoning capabilities of large language models to assess the existence of query triples, which consist of two modules.
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting.
Hyperbolic Fine-tuning for Large Language Models
Through extensive experiments, we demonstrate that HypLoRA significantly enhances the performance of LLMs on reasoning tasks, particularly for complex reasoning problems.
Recent Advances in Speech Language Models: A Survey
To address these issues, Speech Language Models (SpeechLMs) -- end-to-end models that generate speech without converting from text -- have emerged as a promising alternative.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep Graph Anomaly Detection: A Survey and New Perspectives
To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD.
WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents
Watermarking algorithms for large language models (LLMs) have attained high accuracy in detecting LLM-generated text.
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing
Our findings indicate the learning effectiveness of incorporating hashing techniques within the exploration of bipartite graph reception fields, as opposed to simply treating hashing as post-processing to output embeddings.
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
To the best of our knowledge, this is the first work to simultaneously apply hypergraph with node group information and hyperbolic geometry in this field.
Hypformer: Exploring Efficient Hyperbolic Transformer Fully in Hyperbolic Space
Our experimental results confirm the effectiveness and efficiency of Hypformer across various datasets, demonstrating its potential as an effective and scalable solution for large-scale data representation and large models.
Entropy-Based Decoding for Retrieval-Augmented Large Language Models
Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses.
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
By combining the reasoning capabilities of LLMs with the effectiveness of tree search, SeRTS boosts the zero-shot performance of retrieving high-quality and informative results for RAG.
Mitigating Large Language Model Hallucination with Faithful Finetuning
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks.
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
To address these issues, we propose a Biased Progressive Encoding (BPE) paradigm, performing encoding and fusion simultaneously.
A Survey on Vision-Language-Action Models for Embodied AI
Built upon unimodal neural networks, numerous multi-modal models have been introduced to address a range of tasks such as visual question answering, image captioning, and speech recognition.
MarkLLM: An Open-Source Toolkit for LLM Watermarking
However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose challenges for researchers and the community to easily experiment with, understand, and assess the latest advancements.
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients.
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs.
An Entropy-based Text Watermarking Detection Method
From the experiments, we demonstrate that our EWD can achieve better detection performance in low-entropy scenarios, and our method is also general and can be applied to texts with different entropy distributions.
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese.
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis.
Progress and Opportunities of Foundation Models in Bioinformatics
Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs.
HiHPQ: Hierarchical Hyperbolic Product Quantization for Unsupervised Image Retrieval
Existing unsupervised deep product quantization methods primarily aim for the increased similarity between different views of the identical image, whereas the delicate multi-level semantic similarities preserved between images are overlooked.
A Survey of Text Watermarking in the Era of Large Language Models
This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking.
Generalized Category Discovery with Clustering Assignment Consistency
To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency.
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning
We show that in the hyperbolic space one has to address the leaf- and height-level uniformity which are related to properties of trees, whereas in the ambient space of the hyperbolic manifold, these notions translate into imposing an isotropic ring density towards boundaries of Poincar\'e ball.
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogue
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses.
Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?
Making moral judgments is an essential step toward developing ethical AI systems.
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder
The natural language generation domain has witnessed great success thanks to Transformer models.
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities.
Hyperbolic Representation Learning: Revisiting and Advancing
To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i. e., induced hyperbolic norm) to advance existing \hlms.
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule.
Simple Data Augmentation Techniques for Chinese Disease Normalization
Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
In federated heterogeneous graph learning, clients may have private graph schemas.
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models.
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
We propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: (1) adaptive graph convolutional hashing, (2) latent feature dispersion, and (3) Fourier serialized gradient estimation.
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Despite incorporating the target features during training, the teacher model tends to overlook them under the inherent domain shift and task discrepancy.
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society.
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification
Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification.
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.
Momentum Contrastive Pre-training for Question Answering
In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion anSwering (MCROSS) method for extractive QA.
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc.
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Although augmentations (e. g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy.
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges.
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
Hyperbolic Graph Representation Learning: A Tutorial
We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry.
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
HICF: Hyperbolic Informative Collaborative Filtering
The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models.
Accurate RNA 3D structure prediction using a language model-based deep learning approach
Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge.
Graph Component Contrastive Learning for Concept Relatedness Estimation
As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph.
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization).
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
In this paper, we show that the node embedding obtained via the graph augmentations is highly biased, somewhat limiting contrastive models from learning discriminative features for downstream tasks.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text.
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization
Hyperbolic space offers a spacious room to learn embeddings with its negative curvature and metric properties, which can well fit data with tree-like structures.
Text Revision by On-the-Fly Representation Optimization
In this approach, we simply fine-tune a pre-trained Transformer with masked language modeling and attribute classification.
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs
In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
However, in the updating stage, all nodes share the same updating function.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
Towards Efficient Post-training Quantization of Pre-trained Language Models
Experiments on GLUE and SQuAD benchmarks show that our proposed PTQ solution not only performs close to QAT, but also enjoys significant reductions in training time, memory overhead, and data consumption.
Multimodality in Meta-Learning: A Comprehensive Survey
This survey provides a comprehensive overview of the multimodality-based meta-learning landscape in terms of the methodologies and applications.
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation
However, simply integrating KGs in current KG-based RS models is not necessarily a guarantee to improve the recommendation performance, which may even weaken the holistic model capability.
Click-Through Rate Prediction
Knowledge-Aware Recommendation
+1
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.
Controllable Summarization with Constrained Markov Decision Process
We study controllable text summarization which allows users to gain control on a particular attribute (e. g., length limit) of the generated summaries.
Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones.
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry.
A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy
Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score.
A Condense-then-Select Strategy for Text Summarization
This framework first selects salient sentences and then independently condenses each of the selected sentences into a concise version.
Improving the Transferability of Adversarial Samples With Adversarial Transformations
Although deep neural networks (DNNs) have achieved tremendous performance in diverse vision challenges, they are surprisingly susceptible to adversarial examples, which are born of intentionally perturbing benign samples in a human-imperceptible fashion.
Discrete Auto-regressive Variational Attention Models for Text Modeling
We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation
Then, a self-supervised learning framework is constructed: confident predictions from teacher models are served as annotations to guide the student model to learn optical flow for those less confident predictions.
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of practical applications.
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing problem, especially when stacking graph convolution in multiple layers or steps.
Graph-based Semi-supervised Learning: A Comprehensive Review
An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods.
Open-Retrieval Conversational Machine Reading
On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin.
Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum
Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry.
A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
With the rapid development of biomedical software and hardware, a large amount of relational data interlinking genes, proteins, chemical components, drugs, diseases, and symptoms has been collected for modern biomedical research.
BinaryBERT: Pushing the Limit of BERT Quantization
In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization.
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Nevertheless, it is unclear how parameter sharing affects the searching process.
AutoGraph: Automated Graph Neural Network
In this work, we propose a method to automate the deep GNNs design.
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings
Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.
Effective Data-aware Covariance Estimator from Compressed Data
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications.
Making Online Sketching Hashing Even Faster
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data.
Learning 3D Face Reconstruction with a Pose Guidance Network
We present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN).
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation
First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities.
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information.
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning
Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent.
Exploiting Unsupervised Data for Emotion Recognition in Conversations
Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task.
Emotion Recognition in Conversation
General Classification
+2
Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT in identifying emerging app issues, improving the state-of-the-art method by 22. 3% in terms of F1-score.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
Photon: A Robust Cross-Domain Text-to-SQL System
Natural language interfaces to databases (NLIDB) democratize end user access to relational data.
Unsupervised Text Generation by Learning from Search
In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search.
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review.
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
VD-BERT: A Unified Vision and Dialog Transformer with BERT
By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks.
Discrete Variational Attention Models for Language Generation
Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior collapse.
Exclusive Hierarchical Decoding for Deep Keyphrase Generation
A new setting is recently introduced into this problem, in which, given a document, the model needs to predict a set of keyphrases and simultaneously determine the appropriate number of keyphrases to produce.
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching
In this paper, we propose a unified method to jointly learn optical flow and stereo matching.
Automating App Review Response Generation
Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app.
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
Ranked #1 on
Node Clustering
on IMDb
Few Shot Network Compression via Cross Distillation
The core challenge of few shot network compression lies in high estimation errors from the original network during inference, since the compressed network can easily over-fits on the few training instances.
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
We propose an Attention Gated Hierarchical Memory Network (AGHMN) to address the problems of prior work: (1) Commonly used convolutional neural networks (CNNs) for utterance feature extraction are less compatible in the memory modules; (2) Unidirectional gated recurrent units (GRUs) only allow each historical utterance to have context before it, preventing information propagation in the opposite direction; (3) The Soft Attention for summarizing loses the positional and ordering information of memories, regardless of how the memory bank is built.
Ranked #50 on
Emotion Recognition in Conversation
on IEMOCAP
Improving Word Representations: A Sub-sampled Unigram Distribution for Negative Sampling
Word2Vec is the most popular model for word representation and has been widely investigated in literature.
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion Recognition
Witnessing the success of transfer learning in natural language process (NLP), we propose to pre-train a context-dependent encoder (CoDE) for ULER by learning from unlabeled conversation data.
Improving Question Generation With to the Point Context
Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence.
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history.
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases.
Topic-Aware Neural Keyphrase Generation for Social Media Language
Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural Networks.
Microblog Hashtag Generation via Encoding Conversation Contexts
Automatic hashtag annotation plays an important role in content understanding for microblog posts.
SelFlow: Self-Supervised Learning of Optical Flow
We present a self-supervised learning approach for optical flow.
Ranked #7 on
Optical Flow Estimation
on KITTI 2012
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition
In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured.
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations.
DDFlow: Learning Optical Flow with Unlabeled Data Distillation
We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data.
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
In this paper, under a weaker assumption on noises, we study the problem of \underline{lin}ear stochastic {\underline b}andits with h{\underline e}avy-{\underline t}ailed payoffs (LinBET), where the distributions have finite moments of order $1+\epsilon$, for some $\epsilon\in (0, 1]$.
Thread Popularity Prediction and Tracking with a Permutation-invariant Model
This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments.
Topic Memory Networks for Short Text Classification
Many classification models work poorly on short texts due to data sparsity.
Generating Distractors for Reading Comprehension Questions from Real Examinations
We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.
Title-Guided Encoding for Keyphrase Generation
Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP).
Difficulty Controllable Generation of Reading Comprehension Questions
We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG).
DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation
Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers.
Cryptography and Security
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Code Completion with Neural Attention and Pointer Networks
Intelligent code completion has become an essential research task to accelerate modern software development.
Semantically Consistent Image Completion with Fine-grained Details
Image completion has achieved significant progress due to advances in generative adversarial networks (GANs).
High-Resolution Deep Convolutional Generative Adversarial Networks
Generative Adversarial Networks (GANs) convergence in a high-resolution setting with a computational constrain of GPU memory capacity (from 12GB to 24 GB) has been beset with difficulty due to the known lack of convergence rate stability.
Ranked #1 on
Image Generation
on CelebA 128x128
(MS-SSIM metric)
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect.
Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Estimating covariance matrices is a fundamental technique in various domains, most notably in machine learning and signal processing.
Learning to Rank Using Localized Geometric Mean Metrics
First, we design a concept called \textit{ideal candidate document} to introduce metric learning algorithm to query-independent model.
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.
A Survey of Point-of-interest Recommendation in Location-based Social Networks
Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation.
Combinatorial Pure Exploration of Multi-Armed Bandits
We also establish a general problem-dependent lower bound for the CPE problem.
Exact and Stable Recovery of Pairwise Interaction Tensors
For the noisy cases, we also prove error bounds for a constrained convex program for recovering the tensors.
