Search Results for author:
Found 44 papers, 17 papers with code
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates.
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments
After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention.
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training.
Modeling User Viewing Flow using Large Language Models for Article Recommendation
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles.
Unlock Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
Our further analyses show that the visual module plugin method is tailored to enable the image understanding ability for an existing dense retrieval model.
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
ONNXExplainer: an ONNX Based Generic Framework to Explain Neural Networks Using Shapley Values
SHAP package is a leading implementation of Shapley values to explain neural networks implemented in TensorFlow or PyTorch but lacks cross-platform support, one-shot deployment and is highly inefficient.
DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis
This paper introduces an improved duration informed attention neural network (DurIAN-E) for expressive and high-fidelity text-to-speech (TTS) synthesis.
Rep2wav: Noise Robust text-to-speech Using self-supervised representations
Noise-robust TTS models are often trained using the enhanced speech, which thus suffer from speech distortion and background noise that affect the quality of the synthesized speech.
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
AgentBench: Evaluating LLMs as Agents
We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna.
Ranked #1 on
Named Entity Recognition
on ACE 2005
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Clinical trial matching is a key process in health delivery and discovery.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Mind2Web: Towards a Generalist Agent for the Web
We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website.
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images
Keypoint detection and matching is a fundamental task in many computer vision problems, from shape reconstruction, to structure from motion, to AR/VR applications and robotics.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
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)
Eeg2vec: Self-Supervised Electroencephalographic Representation Learning
Recently, many efforts have been made to explore how the brain processes speech using electroencephalographic (EEG) signals, where deep learning-based approaches were shown to be applicable in this field.
Few-shot In-context Learning for Knowledge Base Question Answering
On GrailQA and WebQSP, our model is also on par with other fully-trained models.
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data.
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects.
Event-Triggered Extended State Observer Based Distributed Control of Nonlinear Vehicle Platoons
Finally, we give the range of the control parameters to ensure the stability of the vehicle platoon system.
Knowledge Base Question Answering: A Semantic Parsing Perspective
In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges.
Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China
Housing quality is an essential proxy for regional wealth, security and health.
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers.
Ranked #1 on
Information Retrieval
on MS MARCO
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework.
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking.
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span.
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
A Systematic Investigation of KB-Text Embedding Alignment at Scale
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way.
Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization
The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied.
Search Planning of a UAV/UGV Team with Localization Uncertainty in a Subterranean Environment
We present a waypoint planning algorithm for an unmanned aerial vehicle (UAV) that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment.
Simultaneous Localization and Mapping
Trajectory Planning
Robotics
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition
In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care.
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64, 331 questions, GrailQA, and provide evaluation settings for all three levels of generalization.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Ranked #2 on
Participant Intervention Comparison Outcome Extraction
on EBM-NLP
(using extra training data)
ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders
This paper presents ByteSing, a Chinese singing voice synthesis (SVS) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders.
Cooperative Extended State Observer Based Control of Vehicle Platoons With Arbitrarily Small Time Headway
By using the stability theory of perturbed linear systems, we show that the control parameters can be properly designed to ensure the closed-loop and L2 string stabilities for any given positive time headway.
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.
Improved Planetary Rover Inertial Navigation and Wheel Odometry Performance through Periodic Use of Zero-Type Constraints
We present an approach to enhance wheeled planetary rover dead-reckoning localization performance by leveraging the use of zero-type constraint equations in the navigation filter.
Robotics
Single-Pass PCA of Large High-Dimensional Data
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning.
