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Found 386 papers, 152 papers with code
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.
An Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving
Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information.
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning
Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training.
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation
CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News
We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists.
LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases
Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack
To reduce the potential side effects of using defense modules, we further propose a novel forgetting restricted adversarial training, which filters out bad adversarial examples that impair the performance of original ones.
A Progressive Framework for Role-Aware Rumor Resolution
Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation.
A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving
With the advancements in natural language processing tasks, math word problem solving has received increasing attention.
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks
Specifically, we re-formulate both token and sentence classification tasks into a unified language modeling task, and map label spaces of different tasks into the same vocabulary space.
A Structure-Aware Argument Encoder for Literature Discourse Analysis
Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context.
Iterative GNN-based Decoder for Question Generation
However, (1) they ignore the rich structure information that is hidden in the previously generated text.
PathQG: Neural Question Generation from Facts
Human evaluation also proves that our model is able to generate relevant and informative questions.
Length Generalization of Causal Transformers without Position Encoding
Generalizing to longer sentences is important for recent Transformer-based language models.
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation.
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors.
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models
The generated demos strategically interpolate between existing demos and the given query, transforming the query from OOD to ID.
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Specifically, to solve the challenges due to dependence among adaptive update, unbounded gradient estimate and Lipschitz constant, we demonstrate that the first-order term in the descent lemma converges and its denominator is upper bounded by a function of gradient norm.
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
We prove that our algorithm finds an $\epsilon$-stationary point with a computational complexity of $\mathcal O(\epsilon^{-3k_*-5})$, where $k_*$ is the parameter of the Cressie-Read divergence.
Information Cascade Prediction under Public Emergencies: A Survey
With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies.
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals
We first empirically show that the features of either clean signals or adversarial perturbations are redundant and span in low-dimensional linear subspaces respectively with minimal overlap, and the classical low-dimensional subspace projection can suppress perturbation features out of the subspace of clean signals.
Non-negative Contrastive Learning
In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features.
UV Gaussians: Joint Learning of Mesh Deformation and Gaussian Textures for Human Avatar Modeling
Therefore, we propose UV Gaussians, which models the 3D human body by jointly learning mesh deformations and 2D UV-space Gaussian textures.
EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration
This is because 3DGS treats each pixel as an isolated, single point rather than as an area, causing insensitivity to changes in the footprints of pixels.
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong.
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs).
RECOST: External Knowledge Guided Data-efficient Instruction Tuning
When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples.
Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides
Effective incident management is pivotal for the smooth operation of enterprises-level cloud services.
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
Adversarial misuse, particularly through `jailbreaking' that circumvents a model's safety and ethical protocols, poses a significant challenge for Large Language Models (LLMs).
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions
In this paper, drawing inspiration from recent works that LLMs are sensitive to the design of the instructions, we utilize instructions in code style, which are more structural and less ambiguous, to replace typically natural language instructions.
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations.
Deep Coupling Network For Multivariate Time Series Forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
Unveiling Linguistic Regions in Large Language Models
Furthermore, this core region exhibits significant dimensional dependency, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence.
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated text, we leverage the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain.
On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe
Here we re-purpose the reverse dictionary task as a case study to probe LLMs' capacity for conceptual inference.
$Se^2$: Sequential Example Selection for In-Context Learning
The remarkable capability of large language models (LLMs) for in-context learning (ICL) needs to be activated by demonstration examples.
Text Diffusion with Reinforced Conditioning
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio.
MSynFD: Multi-hop Syntax aware Fake News Detection
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Recent advancements in large language models (LLMs) have opened new pathways for many domains.
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution
To address this issue, we propose a cost-effective preference learning strategy, optimizing reward models by distinguishing between human and machine translations.
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks.
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios.
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences due to out-of-distribution (OOD) issues, we allow each head to process in-distribution length by selecting and attending to important context chunks.
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs.
PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph
Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information.
Are Large Language Models Good Prompt Optimizers?
Despite the success, the underlying mechanism of this approach remains unexplored, and the true effectiveness of LLMs as Prompt Optimizers requires further validation.
DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions.
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
The advancement of large language models (LLMs) has significantly propelled the field of code generation.
Advances in 3D Generation: A Survey
In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications.
Navigating the OverKill in Large Language Models
Large language models are meticulously aligned to be both helpful and harmless.
MouSi: Poly-Visual-Expert Vision-Language Models
This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs.
Ranked #39 on
Visual Question Answering
on MM-Vet
Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback
This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences.
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
To effectively trade-off communication, computation, and inference accuracy, we design a reward function and formulate the offloading problem of CNN inference as a maximization problem with the goal of maximizing the average inference accuracy and throughput over the long term.
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
To bridge this gap, we introduce RoTBench, a multi-level benchmark for evaluating the robustness of LLMs in tool learning.
Improving Domain Adaptation through Extended-Text Reading Comprehension
To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method.
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to enhance resource efficiency while ensuring safety against resource congestion risk.
Contrastive Learning with Negative Sampling Correction
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
Secrets of RLHF in Large Language Models Part II: Reward Modeling
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data.
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD).
LLaMA Beyond English: An Empirical Study on Language Capability Transfer
In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex tasks.
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
Furthermore, a sole emphasis on outcomes disregards the intricate capabilities essential for LLMs to effectively utilize tools.
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks.
Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy.
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media.
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields.
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.
LLMEval: A Preliminary Study on How to Evaluate Large Language Models
The three crucial questions for LLM evaluation are ``what, where, and how to evaluate''.
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation
Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks.
Lite-Mind: Towards Efficient and Robust Brain Representation Network
Research in decoding visual information from the brain, particularly through the non-invasive fMRI method, is rapidly progressing.
FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functions
Implicit Neural Representation (INR), which utilizes a neural network to map coordinate inputs to corresponding attributes, is causing a revolution in the field of signal processing.
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
In this paper, we propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments.
ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis
In this work, we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner.
HumanRef: Single Image to 3D Human Generation via Reference-Guided Diffusion
Optimization-based methods that lift text-to-image diffusion models to 3D generation often fail to preserve the texture details of the reference image, resulting in inconsistent appearances in different views.
GS-IR: 3D Gaussian Splatting for Inverse Rendering
We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results.
Graph Signal Diffusion Model for Collaborative Filtering
In this paper, we make novel adaptions of diffusion model and propose Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
This method trains the model to prioritize the best responses from a pool of candidates created for a particular task.
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
Making Harmful Behaviors Unlearnable for Large Language Models
Specifically, we introduce ``security vectors'', a few new parameters that can be separated from the LLM, to ensure LLM's responses are consistent with the harmful behavior.
Unveiling A Core Linguistic Region in Large Language Models
We have discovered a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters.
Orthogonal Subspace Learning for Language Model Continual Learning
In this paper, we propose orthogonal low-rank adaptation (O-LoRA), a simple and efficient approach for continual learning in language models, effectively mitigating catastrophic forgetting while learning new tasks.
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization
We then propose a meta-learning-based method to learn to align conceptual spaces of different languages, which facilitates zero-shot and few-shot generalization in concept classification and also offers insights into the cross-lingual in-context learning phenomenon.
Auto Search Indexer for End-to-End Document Retrieval
Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents.
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning
In this work, we propose a novel approach that can learn a consistent policy via RL across various data groups or domains.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Ranked #1 on
Entity Linking
on FUNSD
RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms
Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors.
Image super-resolution via dynamic network
In this paper, we present a dynamic network for image super-resolution (DSRNet), which contains a residual enhancement block, wide enhancement block, feature refinement block and construction block.
Ranked #49 on
Image Super-Resolution
on Set14 - 4x upscaling
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars.
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
In this paper, we introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG.
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.
Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
This study serves as a clarion call for a collective effort to overhaul and fortify the safety of open-source LLMs against malicious attackers.
Towards Domain-Specific Features Disentanglement for Domain Generalization
Distributional shift between domains poses great challenges to modern machine learning algorithms.
HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation
The main idea is to enhance the model's 2D perception of 3D geometry by learning a normal-adapted diffusion model and a normal-aligned diffusion model.
Calibrating LLM-Based Evaluator
Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation.
Model-enhanced Vector Index
We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.
RHINO: Regularizing the Hash-based Implicit Neural Representation
This connection ensures a seamless backpropagation of gradients from the network's output back to the input coordinates, thereby enhancing regularization.
Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios
In this work, we use the communication of intent as a means to facilitate cooperation between autonomous vehicle agents.
Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail
Drawing inspiration from voxel-based representations with the level of detail (LoD), we introduce a multi-scale tri-plane-based scene representation that is capable of capturing the LoD of the signed distance function (SDF) and the space radiance.
Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction
We then select them in a domain-aware way to promote informative features for different domains.
Syntax Tree Constrained Graph Network for Visual Question Answering
Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question.
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending
In fact, anomalous behaviors harming long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention unveiled by our work.
The Rise and Potential of Large Language Model Based Agents: A Survey
Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks.
An Empirical Study of NetOps Capability of Pre-Trained Large Language Models
Nowadays, the versatile capabilities of Pre-trained Large Language Models (LLMs) have attracted much attention from the industry.
One-Bit-Aided Modulo Sampling for DOA Estimation
To overcome this fundamental bottleneck, we propose a one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival (DOA) estimation.
VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation
In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion.
${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Identification and analysis of symmetrical patterns in the natural world have led to significant discoveries across various scientific fields, such as the formulation of gravitational laws in physics and advancements in the study of chemical structures.
Data-driven decision-focused surrogate modeling
We introduce the concept of decision-focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real-time settings.
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection
Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization
Adversarial training is one of the best-performing methods in improving the robustness of deep language models.
On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection
Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications.
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference
Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with input sequence length.
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning
The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design.
Successive one-sided Hodrick-Prescott filter with incremental filtering algorithm for nonlinear economic time series
We propose a successive one-sided Hodrick-Prescott (SOHP) filter from multiple time scale decomposition perspective to derive trend estimate for a time series.
Process Knowledge-infused Learning for Clinician-friendly Explanations
In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.
Explainable Artificial Intelligence (XAI)
Language Modelling
Open Set Relation Extraction via Unknown-Aware Training
Inspired by text adversarial attacks, we adaptively apply small but critical perturbations to original training instances and thus synthesizing negative instances that are more likely to be mistaken by the model as known relations.
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
However, general matching methods lack explicit modeling of the above matching pattern.
Actively Supervised Clustering for Open Relation Extraction
The key to the setting is selecting which instances to label.
On the Generalization of Multi-modal Contrastive Learning
Multi-modal contrastive learning (MMCL) has recently garnered considerable interest due to its superior performance in visual tasks, achieved by embedding multi-modal data, such as visual-language pairs.
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Executing actions in a correlated manner is a common strategy for human coordination that often leads to better cooperation, which is also potentially beneficial for cooperative multi-agent reinforcement learning (MARL).
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network
Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.
Towards Better Entity Linking with Multi-View Enhanced Distillation
Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement
To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales.
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Embedding models have shown great power in knowledge graph completion (KGC) task.
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs
The results demonstrate the effectiveness of our method on logical reasoning over KGs in both inductive and transductive settings.
Causal Intervention for Abstractive Related Work Generation
Abstractive related work generation has attracted increasing attention in generating coherent related work that better helps readers grasp the background in the current research.
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
Pretrained language models have achieved remarkable success in various natural language processing tasks.
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition
This paper studies the computational offloading of video action recognition in edge computing.
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization
Models trained with empirical risk minimization (ERM) are revealed to easily rely on spurious correlations, resulting in poor generalization.
Movie101: A New Movie Understanding Benchmark
Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking.
DMDD: A Large-Scale Dataset for Dataset Mentions Detection
We use DMDD to establish baseline performance for dataset mention detection and linking.
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting.
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding
Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding.
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions.
Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild
The principle of continual relation extraction~(CRE) involves adapting to emerging novel relations while preserving od knowledge.
Pre-training Language Model as a Multi-perspective Course Learner
Besides, two self-correction courses are proposed to bridge the chasm between the two encoders by creating a "correction notebook" for secondary-supervision.
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing
Dataset bias, i. e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models.
UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation
Besides, we propose B-Seg, a building instance segmentation method to establish UrbanBIS.
Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees
Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET.
Local Implicit Ray Function for Generalizable Radiance Field Representation
To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray.
Inverting the Imaging Process by Learning an Implicit Camera Model
In principle, our new implicit neural camera model has the potential to benefit a wide array of other inverse imaging tasks.
NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images.
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Large language models have unlocked strong multi-task capabilities from reading instructive prompts.
Ranked #2 on
Zero-shot Named Entity Recognition (NER)
on CrossNER
(using extra training data)
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
Disorder-invariant Implicit Neural Representation
However, the expressive power of INR is limited by the spectral bias in the network training.
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.
IRGen: Generative Modeling for Image Retrieval
While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored.
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generalization.
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics.
Visual Perception System for Autonomous Driving
The recent surge in interest in autonomous driving stems from its rapidly developing capacity to enhance safety, efficiency, and convenience.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.
Natural Language Inference
Natural Language Understanding
+1
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches.
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration
Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern.
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
Study of Optical Networks, 5G, Artificial Intelligence and Their Applications
This paper discusses the application of artificial intelligence (AI) technology in optical communication networks and 5G.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks.
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization
Local search is an effective method for solving large-scale combinatorial optimization problems, and it has made remarkable progress in recent years through several subtle mechanisms.
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
SLAN: Self-Locator Aided Network for Vision-Language Understanding
By aggregating vision-language information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.
VL-Match: Enhancing Vision-Language Pretraining with Token-Level and Instance-Level Matching
Vision-Language Pretraining (VLP) has significantly improved the performance of various vision-language tasks with the matching of images and texts.
Wide-Angle Rectification via Content-Aware Conformal Mapping
Despite the proliferation of ultra wide-angle lenses on smartphone cameras, such lenses often come with severe image distortion (e. g. curved linear structure, unnaturally skewed faces).
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?
We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.
SLAN: Self-Locator Aided Network for Cross-Modal Understanding
By aggregating cross-modal information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance.
Fine-Grained Face Swapping via Regional GAN Inversion
We rethink face swapping from the perspective of fine-grained face editing, \textit{i. e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components.
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding.
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing
We design a feature compression module based on the channel attention method in CNN, to compress the intermediate data by selecting the most important features.
Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields
Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis; however, the requirement of accurate camera poses limits its application.
DINER: Disorder-Invariant Implicit Neural Representation
Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems.
Efficient Adversarial Training with Robust Early-Bird Tickets
Adversarial training is one of the most powerful methods to improve the robustness of pre-trained language models (PLMs).
Towards Understanding Omission in Dialogue Summarization
By analyzing this dataset, we find that a large improvement in summarization quality can be achieved by providing ground-truth omission labels for the summarization model to recover omission information, which demonstrates the importance of omission detection for omission mitigation in dialogue summarization.
Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling
Each score is derived from machine perceptual differences between original and compressed images.
Robust Lottery Tickets for Pre-trained Language Models
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models.
Line Spectral Estimation via Unlimited Sampling
In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm.
Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization
In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model.
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
To solve the maximization problem, we propose a graph reinforcement learning-based early-exit mechanism (GRLE), which outperforms the state-of-the-art work, deep reinforcement learning-based online offloading (DROO) and its enhanced method, DROO with early-exit mechanism (DROOE), under different dynamic scenarios.
Data-Driven Quickest Change Detection in (Hidden) Markov Models
The WADD is at most of the order of the logarithm of the ARL.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Masked Autoencoders (MAE) based on a reconstruction task have risen to be a promising paradigm for self-supervised learning (SSL) and achieve state-of-the-art performance across different benchmark datasets.
Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding
Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models.
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search
Sponsored search advertisements (ads) appear next to search results when consumers look for products and services on search engines.
Ranked #3 on
Image-text matching
on CommercialAdsDataset
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
In this work, we conduct an empirical study on the "Unlabeled Entity Problem" and find that it leads to severe confusion between "O" and entities, decreasing class discrimination of old classes and declining the model's ability to learn new classes.
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited devices.
An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters
Story ending generation aims at generating reasonable endings for a given story context.
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time.
