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Found 75 papers, 28 papers with code
Social Bot-Aware Graph Neural Network for Early Rumor Detection
SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors.
Adaptive Threshold Selective Self-Attention for Chinese NER
Recently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context.
Chinese Named Entity Recognition
named-entity-recognition
+2
Randomized Quasi-Monte Carlo Features for Kernel Approximation
We investigate the application of randomized quasi-Monte Carlo (RQMC) methods in random feature approximations for kernel-based learning.
H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging
To our knowledge, H3DE-Net is the first 3D landmark detection model that integrates such a lightweight attention mechanism with CNNs.
Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities
The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms.
LIMO: Less is More for Reasoning
While conventional wisdom suggests that sophisticated reasoning tasks demand extensive training data (>100, 000 examples), we demonstrate that complex mathematical reasoning abilities can be effectively elicited with surprisingly few examples.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning
Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the potential of inference-time scaling in large language models (LLMs) for medical reasoning tasks, ranging from diagnostic decision-making to treatment planning.
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection
Anatomical landmark detection (ALD) from a medical image is crucial for a wide array of clinical applications.
O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques.
RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering
To address these issues, we propose Rule-Guided Retrieval-Augmented Generation with LMs, which explicitly introduces symbolic rules as demonstrations for in-context learning (RuleRAG-ICL) to guide retrievers to retrieve logically related documents in the directions of rules and uniformly guide generators to generate answers attributed by the guidance of the same set of rules.
O1 Replication Journey: A Strategic Progress Report -- Part 1
This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey.
Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling
Recent studies on detecting hallucinations in long text without external resources conduct consistency comparison among multiple sampled outputs.
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a speech input.
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Recent works propose to employ supervised fine-tuning (SFT) to mitigate the sycophancy issue, while it typically leads to the degeneration of LLMs' general capability.
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and attention-based encoder-decoder (AED) loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning
Specifically, in this work, through rigorously modeling the training process and deriving an upper bound on FEEL's one-round convergence rate, we establish a problem of joint resource allocation and data selection, which, unfortunately, cannot be solved directly.
OlympicArena Medal Ranks: Who Is the Most Intelligent AI So Far?
In this report, we pose the following question: Who is the most intelligent AI model to date, as measured by the OlympicArena (an Olympic-level, multi-discipline, multi-modal benchmark for superintelligent AI)?
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions.
Adaptive Cooperative Streaming of Holographic Video Over Wireless Networks: A Proximal Policy Optimization Solution
Adapting holographic video streaming to fluctuating wireless channels is essential to maintain consistent and satisfactory Quality of Experience (QoE) for users, which, however, is a challenging task due to the dynamic and uncertain characteristics of wireless networks.
Enhancing CTC-based speech recognition with diverse modeling units
In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transformer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
In this paper, we perceive the LLMs' KB with SoeQ by discovering more ambiguous answers.
Vulnerability Detection in C/C++ Code with Deep Learning
Our result shows that combining different types of characteristics of source code and using a balanced number of vulnerable program slices and non-vulnerable program slices produce a balanced accuracy in predicting both vulnerable code and non-vulnerable code.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions
Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions, which are frequently applied to emoji and poster generation.
Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game
In this paper, we propose a multi-agent attacker-disguiser game approach to achieve a weak defense mechanism that allows the large model to both safely reply to the attacker and hide the defense intent.
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.
Automating psychological hypothesis generation with AI: when large language models meet causal graph
Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology.
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation
POMP involves constructing a directed acyclic meta-graph for each source language, from which we dynamically sample multiple paths to prompt LLMs to mitigate the linguistic noise and improve translations during training.
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design
Text-driven fashion synthesis and design is an extremely valuable part of artificial intelligence generative content(AIGC), which has the potential to propel a tremendous revolution in the traditional fashion industry.
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge.
Acoustic Model Fusion for End-to-end Speech Recognition
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence
Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility.
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
To this end, we employ domain adversarial learning as a heuristic neural network initialization method, which can help the meta-learning module converge to a better optimal.
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification
Theoretically, we prove the convergence of the meta-learning algorithm in MTEM and analyze the effectiveness of MTEM in achieving domain adaptation.
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
The pelvis, the lower part of the trunk, supports and balances the trunk.
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks.
Sequential Recommendation with Diffusion Models
Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation.
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs).
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph
Different from KGs and TKGs in the transductive setting, constantly emerging entities and relations in incomplete TKGs create demand to predict missing facts with unseen components, which is the extrapolation setting.
Teaching What You Should Teach: A Data-Based Distillation Method
To be specific, we design a neural network-based data augmentation module with priori bias, which assists in finding what meets the teacher's strengths but the student's weaknesses, by learning magnitudes and probabilities to generate suitable data samples.
R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference
Document-level natural language inference (DOCNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents.
A Treatise On FST Lattice Based MMI Training
Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models.
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification
Then we integrate the multi-view contextual information to encode the evidence sentences to handle the task.
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks.
Toward Real-world Single Image Deraining: A New Benchmark and Beyond
To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1, 120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively.
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
Specifically, we collect a sheer number of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated.
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE).
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching
To achieve such an ambitious goal, new mechanisms for foreign pronunciation generation and language model (LM) enrichment have been devised.
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
Ranked #5 on
Person Re-Identification
on VC-Clothes
Cloth-Changing Person Re-Identification
Computational Efficiency
+1
3D Local Convolutional Neural Networks for Gait Recognition
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
Ranked #4 on
Gait Recognition
on OUMVLP
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification
We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments.
Argumentation Mining on Essays at Multi Scales
Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition.
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
Next Waves in Veridical Network Embedding
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.
Topological Hall Effect and Skyrmion-like Bubbles at a Charge-transfer Interface
Here, the emergence, tuning and interpretation of hump-shape Hall Effect from a CaMnO3/CaIrO3/CaMnO3 trilayer structure are studied in detail.
Mesoscale and Nanoscale Physics
Language Model-Driven Unsupervised Neural Machine Translation
We present the detailed mathematical construction of our method.
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding
However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese.
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN).
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning
Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.
Ranked #8 on
Question Answering
on DROP Test
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
This paper considers the reading comprehension task in which multiple documents are given as input.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence.
Aspect-Based Sentiment Analysis (ABSA)
Aspect Term Extraction and Sentiment Classification
+2
Attention-Guided Answer Distillation for Machine Reading Comprehension
Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred.
Ranked #11 on
Question Answering
on SQuAD2.0 dev
Free-rider Episode Screening via Dual Partition Model
An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently.
Ocasta: Clustering Configuration Settings For Error Recovery
However, a recent study found that a significant amount of configuration errors require fixing more than one setting together.
BinPro: A Tool for Binary Source Code Provenance
Enforcing open source licenses such as the GNU General Public License (GPL), analyzing a binary for possible vulnerabilities, and code maintenance are all situations where it is useful to be able to determine the source code provenance of a binary.
Cryptography and Security D.4.6
Reinforced Mnemonic Reader for Machine Reading Comprehension
In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
Ranked #17 on
Question Answering
on SQuAD1.1 dev
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals.
Sound
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Feature Selection with Redundancy-complementariness Dispersion
Feature selection has attracted significant attention in data mining and machine learning in the past decades.
