Search Results for author:
Found 59 papers, 27 papers with code
Simple Multigraph Convolution Networks
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators.
FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model
However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news.
Ranked #1 on
Stock Market Prediction
on Astock
Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model
Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers.
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
By revealing the limitations of existing LALMs through evaluation results, AIR-Bench can provide insights into the direction of future research.
SIG: Speaker Identification in Literature via Prompt-Based Generation
Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context.
Best Arm Identification in Batched Multi-armed Bandit Problems
The linear program leads to a two-stage algorithm that can achieve good theoretical properties.
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
Ranked #1 on
Acoustic Scene Classification
on TUT Acoustic Scenes 2017
(using extra training data)
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
In this paper, we propose LauraGPT, a unified GPT model for audio recognition, understanding, and generation.
NP$^2$L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks
Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs).
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
The Whole Pathological Slide Classification via Weakly Supervised Learning
Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis.
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
To improve the exploitation of the structural information, we propose a novel entity alignment framework called Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three dimensions : (i) Model.
OTFDM: A Novel 2D Modulation Waveform Modeling Dot-product Doubly-selective Channel
OTFDM uses a 2D dot-product channel model to cope with doubly-selectivity.
Effort Discrimination and Curvature of Contest Technology in Conflict Networks
When the contest technology in each battle is of Tullock form, a surprising neutrality result holds within the class of semi-symmetric conflict network structures: both the aggregate actions and equilibrium payoffs under two regimes are the same.
SBcoyote: An Extensible Python-Based Reaction Editor and Viewer
A unique feature of the tool is the extensive Python plugin API, where third-party developers can include new functionality.
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions.
A universal DNA computing model for solving NP-hard subset problems
Although DNA computing has been exploited to solve various intractable computational problems, such as the Hamiltonian path problem, SAT problem, and graph coloring problem, there has been little discussion of designing universal DNA computing-based models, which can solve a class of problems.
$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
The combination of $n$-gram and neural LMs not only allows the neural part to focus on the deeper understanding of language but also provides a flexible way to customize an LM by switching the underlying $n$-gram model without changing the neural model.
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
While large-scale neural language models, such as GPT2 and BART, have achieved impressive results on various text generation tasks, they tend to get stuck in undesirable sentence-level loops with maximization-based decoding algorithms (\textit{e. g.}, greedy search).
A Novel Markov Model for Near-Term Railway Delay Prediction
We then propose a delay prediction model based on non-homogeneous Markov chains.
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single minibatch to reconstruct the private data used by clients during training.
SBMLDiagrams: A python package to process and visualize SBML layout and render
One of the extensions for SBML is the SBML Layout and Render package.
ECO v1: Towards Event-Centric Opinion Mining
Events are considered as the fundamental building blocks of the world.
Procedural Text Understanding via Scene-Wise Evolution
In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.
libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library
Motivation: This paper presents libRoadRunner 2. 0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language SBML).
CLS: Cross Labeling Supervision for Semi-Supervised Learning
Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.
Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels.
Speech-T: Transducer for Text to Speech and Beyond
Experiments on LJSpeech datasets demonstrate that Speech-T 1) is more robust than the attention based autoregressive TTS model due to its inherent monotonic alignments between text and speech; 2) naturally supports streaming TTS with good voice quality; and 3) enjoys the benefit of joint modeling TTS and ASR in a single network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition
Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect from multiple candidates to better detect and correct error tokens.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Hit-finding: Multilabel Neural Architecture with DEL
DNA-Encoded Libraries (DEL thereafter) data, often with millions of data points, enables large deep learning models to make real contributions in the drug discovery process (e. g., hit-finding).
Analyzing and Mitigating Interference in Neural Architecture Search
In this paper, we investigate the interference issue by sampling different child models and calculating the gradient similarity of shared operators, and observe: 1) the interference on a shared operator between two child models is positively correlated with the number of different operators; 2) the interference is smaller when the inputs and outputs of the shared operator are more similar.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on
Event Extraction
on ACE2005
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source.
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
Group Equivariant Subsampling
We use these layers to construct group equivariant autoencoders (GAEs) that allow us to learn low-dimensional equivariant representations.
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal.
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
The technical challenge of NAS-BERT is that training a big supernet on the pre-training task is extremely costly.
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Task-Agnostic and Adaptive-Size BERT Compression
NAS-BERT trains a big supernet on a carefully designed search space containing various architectures and outputs multiple compressed models with adaptive sizes and latency.
Incorporate Semantic Structures into Machine Translation Evaluation via UCCA
Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence.
OFFER: A Motif Dimensional Framework for Network Representation Learning
In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.
Cognitive Representation Learning of Self-Media Online Article Quality
The automatic quality assessment of self-media online articles is an urgent and new issue, which is of great value to the online recommendation and search.
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition
However, there are more than 6, 000 languages in the world and most languages are lack of speech training data, which poses significant challenges when building TTS and ASR systems for extremely low-resource languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multivariate Relations Aggregation Learning in Social Networks
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #8 on
2D Human Pose Estimation
on COCO-WholeBody
A Bayesian Framework for Nash Equilibrium Inference in Human-Robot Parallel Play
We showed that using a Bayesian approach to infer the equilibrium enables the robot to complete the task with less than half the number of collisions while also reducing the task execution time as compared to the best baseline.
MultiSpeech: Multi-Speaker Text to Speech with Transformer
Transformer-based text to speech (TTS) model (e. g., Transformer TTS~\cite{li2019neural}, FastSpeech~\cite{ren2019fastspeech}) has shown the advantages of training and inference efficiency over RNN-based model (e. g., Tacotron~\cite{shen2018natural}) due to its parallel computation in training and/or inference.
Weak Supervision for Fake News Detection via Reinforcement Learning
In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.
MetaFun: Meta-Learning with Iterative Functional Updates
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one.
Transfer Value Iteration Networks
However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.
The Graph-Based Behavior-Aware Recommendation for Interactive News
First, we build an interaction behavior graph for multi-level and multi-category data.
Immunological recognition by artificial neural networks
To address this problem, we examine whether the affinity-based discrimination of peptide sequences is learnable and generalizable by artificial neural networks (ANNs) that process the digital experimental amino acid sequence information of receptors and peptides.
Controllable Semantic Image Inpainting
We develop a method for user-controllable semantic image inpainting: Given an arbitrary set of observed pixels, the unobserved pixels can be imputed in a user-controllable range of possibilities, each of which is semantically coherent and locally consistent with the observed pixels.
Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching
We examine sequences of 10, 000 human T-cell receptors and 10, 000 antigenic peptides, and obtain a full spectrum of cross-reactivity of the receptor-peptide binding.
A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations
By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions.
Active Dictionary Learning in Sparse Representation Based Classification
The classification accuracy and reconstruction error are used to evaluate the proposed dictionary learning method.
