Search Results for author:
Found 156 papers, 70 papers with code
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Benchmarking Meaning Representations in Neural Semantic Parsing
Thus, the impact of meaning representation on semantic parsing is less understood.
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue
Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base.
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society.
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment.
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs
It maintains model performance while enabling a seamless transition from a standard Video-LLM to a sparse Video-LLM with minimal code modifications.
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
We therefore introduce SWE-Dev, the first large-scale dataset (with 14, 000 training and 500 test samples) designed to evaluate and train autonomous coding systems on real-world feature development tasks.
Impact of Insufficient CP on Sensing Performance in OFDM-ISAC Systems
These expressions quantify the effects of CP length, symbol constellation, and inter-target interference (ITI) on the mainlobe and sidelobe levels.
Sensing-Oriented Adaptive Resource Allocation Designs for OFDM-ISAC Systems
In this paper, we propose adaptive resource allocation strategies for OFDM-ISAC systems to achieve optimal trade-offs between diverse sensing requirements and communication quality-of-service (QoS).
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
DeepSeek-R1 has shown that long chain-of-thought (CoT) reasoning can naturally emerge through a simple reinforcement learning (RL) framework with rule-based rewards, where the training may directly start from the base models-a paradigm referred to as zero RL training.
SkyLadder: Better and Faster Pretraining via Context Window Scheduling
Recent advancements in LLM pretraining have featured ever-expanding context windows to process longer sequences.
Low Range-Doppler Sidelobe ISAC Waveform Design: A Low-Complexity Approach
Integrated sensing and communication (ISAC) is a pivotal enabler for next-generation wireless networks.
Water Quality Data Imputation via A Fast Latent Factorization of Tensors with PID-based Optimizer
Water quality data can supply a substantial decision support for water resources utilization and pollution prevention.
Distributed Distortion-Aware Beamforming Designs for Cell-Free mMIMO Systems
Cell-free massive multi-input multi-output (CF-mMIMO) systems have emerged as a promising paradigm for next-generation wireless communications, offering enhanced spectral efficiency and coverage through distributed antenna arrays.
Tri-timescale Beamforming Design for Tri-hybrid Architectures with Reconfigurable Antennas
Reconfigurable antennas possess the capability to dynamically adjust their fundamental operating characteristics, thereby enhancing system adaptability and performance.
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Through comprehensive experiments with 1B and 3B parameter models, we demonstrate that models trained on 30B tokens selected with PreSelect surpasses the performance of a vanilla baseline trained on 300B tokens, achieving a 10x reduction in compute requirements.
Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
This tutorial presents a comprehensive introduction to Speculative Decoding (SD), an advanced technique for LLM inference acceleration that has garnered significant research interest in recent years.
Characteristics Analysis of Autonomous Vehicle Pre-crash Scenarios
In this paper, we analyzed the latest California AV collision reports and used the newly revised pre-crash scenario typology to identify pre-crash scenarios.
Joint Waveform and Beamforming Design in RIS-ISAC Systems: A Model-Driven Learning Approach
Integrated Sensing and Communication (ISAC) has emerged as a key enabler for future wireless systems.
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines.
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
We present ECG-Expert-QA, a comprehensive multimodal dataset for evaluating diagnostic capabilities in electrocardiogram (ECG) interpretation.
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis.
Improving Your Model Ranking on Chatbot Arena by Vote Rigging
We first introduce a straightforward target-only rigging strategy that focuses on new battles involving $m_{t}$, identifying it via watermarking or a binary classifier, and exclusively voting for $m_{t}$ wins.
Target Detection in OFDM-ISAC Systems: A Multipath Exploitation Approach
This paper investigates the potential of multipath exploitation for enhancing target detection in orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems.
Target Detection in ISAC Systems with Active RISs: A Multi-Perspective Observation Approach
Integrated sensing and communication (ISAC) has emerged as a transformative technology for 6G networks, enabling the seamless integration of communication and sensing functionalities.
Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue
The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored.
Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework
In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks.
RAG for Effective Supply Chain Security Questionnaire Automation
In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative.
SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languages
In this paper, we introduce SailCompass, a reproducible and robust evaluation benchmark for assessing Large Language Models (LLMs) on Southeast Asian Languages (SEA).
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training
To address this, we develop AnchorAttention, a plug-and-play attention method that alleviates numerical issues caused by BFloat16, improves long-context capabilities, and speeds up training.
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
Ranked #1 on
Text-To-SQL
on Spider 2.0
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
Scaling up Masked Diffusion Models on Text
Masked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as text generation and language understanding, remain underexplored.
DataTales: A Benchmark for Real-World Intelligent Data Narration
We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives.
Joint Space-Time Adaptive Processing and Beamforming Design for Cell-Free ISAC Systems
In this paper, we explore cooperative sensing and communication within cell-free integrated sensing and communication (ISAC) systems.
CCSBench: Evaluating Compositional Controllability in LLMs for Scientific Document Summarization
To broaden the dissemination of scientific knowledge to diverse audiences, scientific document summarization must simultaneously control multiple attributes such as length and empirical focus.
DOA Estimation-Oriented Joint Array Partitioning and Beamforming Designs for ISAC Systems
Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks.
When Attention Sink Emerges in Language Models: An Empirical View
In this work, we first demonstrate that attention sinks exist universally in LMs with various inputs, even in small models.
LucidGrasp: Robotic Framework for Autonomous Manipulation of Laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation
The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability.
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets.
Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates
Achieving high win rates on these benchmarks can significantly boost the promotional impact of newly released language models.
Self-supervised Spatio-Temporal Graph Mask-Passing Attention Network for Perceptual Importance Prediction of Multi-point Tactility
While visual and auditory information are prevalent in modern multimedia systems, haptic interaction, e. g., tactile and kinesthetic interaction, provides a unique form of human perception.
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation
Here, we present TUMSyn, a Text-guided Universal MR image Synthesis generalist model, which can flexibly generate brain MR images with demanded imaging metadata from routinely acquired scans guided by text prompts.
Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
Large language model pre-training has traditionally relied on human experts to craft heuristics for improving the corpora quality, resulting in numerous rules developed to date.
Active Reconfigurable Intelligent Surface Empowered Synthetic Aperture Radar Imaging
In this paper, we first present a range-Doppler (RD) imaging algorithm to obtain imaging results for the proposed ARIS-empowered SAR system.
Dynamic Hybrid Beamforming Designs for ELAA Near-Field Communications
Extremely large-scale antenna array (ELAA) is a key candidate technology for the sixth generation (6G) mobile networks.
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and advancement, there are still gaps in defining a more holistic research target seamlessly integrating multimodality, conversation context, fine-granularity, and also covering the changing sentiment dynamics as well as cognitive causal rationales.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
We investigate how vocabulary size impacts LLM scaling laws by training models ranging from 33M to 3B parameters on up to 500B characters with various vocabulary configurations.
Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?
These tasks, derived from real-world use cases, evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
RegMix: Data Mixture as Regression for Language Model Pre-training
RegMix trains many small models on diverse data mixtures, uses regression to predict performance of unseen mixtures, and applies the best predicted mixture to train a large-scale model with orders of magnitude more compute.
MIMO-OFDM ISAC Waveform Design for Range-Doppler Sidelobe Suppression
In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach.
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions.
Ranked #1 on
Code Generation
on BigCodeBench-Instruct
Bootstrapping Language Models with DPO Implicit Rewards
In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM.
Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving.
Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
In addition, we conduct comprehensive and elaborate (e. g., making sure to use correct system prompts) evaluations against other aligned LLMs and advanced defenses, where our method consistently achieves nearly 100% ASRs.
Multipath Exploitation for Fluctuating Target Detection in RIS-Assisted ISAC Systems
Integrated sensing and communication (ISAC) systems are typically deployed in multipath environments, which is usually deemed as a challenging issue for wireless communications.
Faithful Logical Reasoning via Symbolic Chain-of-Thought
Technically, building upon an LLM, SymbCoT 1) first translates the natural language context into the symbolic format, and then 2) derives a step-by-step plan to solve the problem with symbolic logical rules, 3) followed by a verifier to check the translation and reasoning chain.
MANTIS: Interleaved Multi-Image Instruction Tuning
We further evaluate Mantis on single-image benchmarks and demonstrate that Mantis also maintains a strong single-image performance on par with CogVLM and Emu2.
ISQA: Informative Factuality Feedback for Scientific Summarization
We propose Iterative Facuality Refining on Informative Scientific Question-Answering (ISQA) feedback\footnote{Code is available at \url{https://github. com/lizekai-richard/isqa}}, a method following human learning theories that employs model-generated feedback consisting of both positive and negative information.
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
Beyond Memorization: The Challenge of Random Memory Access in Language Models
Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #36 on
Code Generation
on MBPP
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities.
EVOR: Evolving Retrieval for Code Generation
Recently the retrieval-augmented generation (RAG) has been successfully applied in code generation.
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One
Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions.
Purifying Large Language Models by Ensembling a Small Language Model
The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources.
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use.
Test-Time Backdoor Attacks on Multimodal Large Language Models
Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase.
Mercury: A Code Efficiency Benchmark for Code Large Language Models
Based on the distribution, we introduce a new metric Beyond, which computes a runtime-percentile-weighted Pass score to reflect functional correctness and code efficiency simultaneously.
Ocassionally Secure: A Comparative Analysis of Code Generation Assistants
$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example.
UniVision: A Unified Framework for Vision-Centric 3D Perception
The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving.
End-to-End Learning for SLP-Based ISAC Systems
Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform.
A Practical Beamforming Design for Active RIS-assisted MU-MISO Systems
Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics.
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
Through investigations across 5 tasks and 8 different datasets encompassing both code comprehension and code generation tasks, we find that FFT generally leads to the best downstream performance across all scales, and PEFT methods differ significantly in their efficacy based on the model scale.
Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach
Extensive experimental results confirm the effectiveness of our proposed MADRL framework in improving both sensing and communication performance through the utilization of target-mounted STARS.
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.
Cramer-Rao Bound Optimization for Active RIS-Empowered ISAC Systems
Integrated sensing and communication (ISAC), which simultaneously performs sensing and communication functions within a shared frequency band and hardware platform, has emerged as a promising technology for future wireless systems.
OctoPack: Instruction Tuning Code Large Language Models
We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46. 2% pass@1).
A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems
The proposed joint estimation algorithm can achieve larger signal-to-noise-ratio (SNR) processing gains and higher resolution by fully exploiting the echo signals and jointly estimating the angle-range-velocity information.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #2 on
Node Property Prediction
on ogbn-arxiv
Fast algorithms for k-submodular maximization subject to a matroid constraint
In this paper, we apply a Threshold-Decreasing Algorithm to maximize $k$-submodular functions under a matroid constraint, which reduces the query complexity of the algorithm compared to the greedy algorithm with little loss in approximation ratio.
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks.
Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering.
Automotive Object Detection via Learning Sparse Events by Spiking Neurons
Traditional object detection techniques that utilize Artificial Neural Networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture.
Low-Range-Sidelobe Waveform Design for MIMO-OFDM ISAC Systems
Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization.
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence.
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination
In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner.
Generative Table Pre-training Empowers Models for Tabular Prediction
Recently, the topic of table pre-training has attracted considerable research interest.
Active Retrieval Augmented Generation
In this work, we provide a generalized view of active retrieval augmented generation, methods that actively decide when and what to retrieve across the course of the generation.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #54 on
Code Generation
on MBPP
From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Empirical results on various benchmarks validate that the integration of SQL execution leads to significant improvements in zero-shot scenarios, particularly in table reasoning.
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
This is due to the increasing number of smart devices that require sensory processing for their application on the edge.
RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design
Integrated sensing and communication (ISAC) has been envisioned as a promising technique to alleviate the spectrum congestion problem.
Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems
Integrated sensing and communication (ISAC) is recognized as a promising technology with great potential in saving hardware and spectrum resources, since it simultaneously realizes radar detection and user communication functions in the fully-shared platform.
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention.
SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems
Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients.
SantaCoder: don't reach for the stars!
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code.
Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC), which allows individual radar and communication systems to share the same spectrum bands, is an emerging and promising technique for alleviating spectrum congestion problems.
OpenFE: Automated Feature Generation with Expert-level Performance
In the two competitions, features generated by OpenFE with a simple baseline model can beat 99. 3% and 99. 6% data science teams respectively.
Learning on Large-scale Text-attributed Graphs via Variational Inference
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
Ranked #1 on
Node Property Prediction
on ogbn-papers100M
End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation
To overcome the performance bottleneck of these approaches, in this letter we propose an end-to-end learning based approach to jointly optimize the modulation orders, the transmit precoding and the receive detection for an SLP communication system.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems
Numerical reasoning over natural language has been a long-standing goal for the research community.
Joint Beamforming Designs for Active Reconfigurable Intelligent Surface: A Sub-Connected Array Architecture
Affected by the "double fading" effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS.
Optimization for Reflection and Transmission Dual-Functional Active RIS-Assisted Systems
Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices.
Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems
Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment.
Non-Cooperative Resource Management for Intelligent Reflecting Surface Aided Networks
Intelligent reflecting surface (IRS) has emerged as a promising and revolutionizing technology for future wireless networks.
On Grounded Planning for Embodied Tasks with Language Models
Language models (LMs) have demonstrated their capability in possessing commonsense knowledge of the physical world, a crucial aspect of performing tasks in everyday life.
Partially Distributed Beamforming Design for RIS-Aided Cell-Free Networks
Cell-free networks are regarded as a promising technology to meet higher rate requirements for beyond fifth-generation (5G) communications.
User Association and Hybrid Beamforming Designs for Cooperative mmWave MIMO Systems
In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design.
Joint Beamforming Design for RIS-Assisted Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks.
Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions
Integrated sensing and communication (ISAC) is emerging as a key enabler to address the growing spectrum congestion problem and satisfy increasing demands for ubiquitous sensing and communication.
IRS-assisted Multi-cell Multi-band Systems: Practical Reflection Model and Joint Beamforming Design
Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/hardware-efficient manner.
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
This motivates us to propose input-tuning, which fine-tunes both the continuous prompts and the input representations, leading to a more effective way to adapt unfamiliar inputs to frozen PLMs.
Reasoning Like Program Executors
Reasoning over natural language is a long-standing goal for the research community.
Ranked #2 on
Question Answering
on DROP Test
(using extra training data)
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
Image Edge Restoring Filter
In computer vision, image processing and computer graphics, image smoothing filtering is a very basic and important task and to be expected possessing good edge-preserving smoothing property.
Joint Transmit Waveform and Passive Beamforming Design for RIS-Aided DFRC Systems
Reconfigurable intelligent surface (RIS) is a promising technology for 6G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment.
Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space
By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.
Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation
In this paper, we propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language.
Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach
In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA).
Ranked #1 on
Knowledge Base Question Answering
on GrailQA
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
Reflection and Relay Dual-Functional RIS Assisted MU-MISO Systems
Reconfigurable intelligent surfaces (RISs) have been deemed as one of potential components of future wireless communication systems because they can adaptively manipulate the wireless propagation environment with low-cost passive devices.
TAPEX: Table Pre-training via Learning a Neural SQL Executor
TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
Learning Algebraic Recombination for Compositional Generalization
In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization.
Ranked #2 on
Semantic Parsing
on CFQ
BS-RIS-User Association and Beamforming Designs for RIS-aided Cellular Networks
During the dynamical access process, an iterative algorithm is proposed to alternatively obtain the active and passive beamforming.
Simulation on the Transparency of Electrons and Ion Back Flow for a Time Projection Chamber based on Staggered Multiple THGEMs
The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively.
Instrumentation and Detectors High Energy Physics - Experiment
Intelligent reflecting surface assisted multi-cell multi-band wireless networks
Therefore, the practical phase shift model, which can describe the difference of IRS phase shift responses for the signals with different frequencies, should be utilized in the IRS optimization for wideband and multi-band systems.
Channel Estimation for Practical IRS-Assisted OFDM Systems
Different from the prior works which assume that IRS has an ideal reflection model, we perform channel estimation by considering amplitude-phase shift-frequency relationship for the response of practical IRS.
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Incomplete Utterance Rewriting as Semantic Segmentation
Recent years the task of incomplete utterance rewriting has raised a large attention.
Ranked #1 on
Dialogue Rewriting
on Rewrite
Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach
Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion.
Intelligent reflecting surface enhanced wideband MIMO-OFDM communications: From practical model to reflection optimization
Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.
Compositional Generalization by Learning Analytical Expressions
Compositional generalization is a basic and essential intellective capability of human beings, which allows us to recombine known parts readily.
Practical Modeling and Beamforming for Intelligent Reflecting Surface Aided Wideband Systems
In this letter, we aim to investigate the phase-amplitude-frequency relationship of the reflected signals and propose a practical model of reflection coefficient for an IRS-aided wideband system.
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e. g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.
You Impress Me: Dialogue Generation via Mutual Persona Perception
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on
Dialogue Generation
on Persona-Chat
(using extra training data)
CTM: Collaborative Temporal Modeling for Action Recognition
In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure 1) to learn temporal information for action recognition.
iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention
The standard non-local (NL) module is effective in aggregating frame-level features on the task of video classification but presents low parameters efficiency and high computational cost.
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received considerable attention, which is challenging since there are complex contextual phenomena.
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs.
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL
One key component in text-to-SQL is to predict the comparison relations between columns and their values.
A Split-and-Recombine Approach for Follow-up Query Analysis
To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information.
Symbol-Level Precoding Design for Intelligent Reflecting Surface Assisted Multi-user MIMO Systems
In this paper we investigate the problem of precoder design for a low-resolution IRS-based transmitter to implement multi-user MISO/MIMO wireless communications.
FANDA: A Novel Approach to Perform Follow-up Query Analysis
NLIDB allow users to search databases using natural language instead of SQL-like query languages.
Task-oriented Word Embedding for Text Classification
In this paper, we propose a task-oriented word embedding method and apply it to the text classification task.
Ranked #23 on
Text Classification
on AG News
Noisy Softplus: an activation function that enables SNNs to be trained as ANNs
We extended the work of proposed activation function, Noisy Softplus, to fit into training of layered up spiking neural networks (SNNs).
Biclustering Via Sparse Clustering
This framework can be used to identify biclusters that differ with respect to the means of the features, the variance of the features, or more general differences.
