Search Results for author:
Found 704 papers, 229 papers with code
A Bayesian Topic Model for Human-Evaluated Interpretability
Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model.
Robust Graph Representation Learning via Neural Sparsification
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Abstractive Text Summarization
Conversational Response Generation
+8
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations
The emotion cause is a stimulus for human emotions.
AllWeatherNet:Unified Image enhancement for autonomous driving under adverse weather and lowlight-conditions
AllWeather-Net effectively transforms images into normal weather and daytime scenes, demonstrating superior image enhancement results and subsequently enhancing the performance of semantic segmentation, with up to a 5. 3% improvement in mIoU in the trained domain.
PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View
In this paper, in order to adapt the image information distribution and preserve the view symmetry by regular convolution, we propose to employ the polar BEV representation to substitute the Cartesian BEV representation.
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content
The first is super-resolution with deferred rendering, to support the gaming solution of rendering and transferring LR images only and restoring HR images on the client side.
BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Compared to single agents, multi-agent systems have higher requirements for the collaboration capabilities of language models.
Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion
Based on this observation, we introduce the Collaborative Decision Making (CDM) module, which fuses the multiple classifier heads to enhance the inference performance of adaptive deep networks.
TVG: A Training-free Transition Video Generation Method with Diffusion Models
Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives.
HabitAction: A Video Dataset for Human Habitual Behavior Recognition
Experimental results demonstrate that our proposed method has much better performance in action recognition than the existing methods on the proposed dataset.
CathAction: A Benchmark for Endovascular Intervention Understanding
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions.
ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
Understanding biological processes, drug development, and biotechnological advancements requires detailed analysis of protein structures and sequences, a task in protein research that is inherently complex and time-consuming when performed manually.
BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation
This involves adaptively sampling the vibration signals based on the sampling rate of the sensor, incorporating the frequency domain to unify input dimensions, and using a fault-free reference signal as an auxiliary input.
Security Attacks on LLM-based Code Completion Tools
The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs).
S^3D-NeRF: Single-Shot Speech-Driven Neural Radiance Field for High Fidelity Talking Head Synthesis
Talking head synthesis is a practical technique with wide applications.
Language-Driven Interactive Shadow Detection
Based on this dataset, we propose a Referring Shadow-Track Memory Network (RSM-Net) for addressing the RVSD task.
Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization
Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data.
Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning
Based on the Deep- SPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i. e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB.
CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization
Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial information to quickly detect and respond to cyber threats in the cybersecurity domain.
Inverse designing metamaterials with programmable nonlinear functional responses in graph space
Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, and acoustic and photonic bandgaps.
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
In this paper, we propose UnifiedMLLM, a comprehensive model designed to represent various tasks using a unified representation.
Composite Learning Adaptive Control without Excitation Condition
In comparison to the existing results, all spectrums of previously appeared excitation information are collected, with the matrices in linear regression equation guaranteed to be bounded.
Adaptive Safety with Control BarrierFunctions and Triggered Batch Least-Squares Identifier
In this paper, a triggered Batch Least-Squares Identifier (BaLSI) based adaptive safety control scheme is proposed for uncertain systems with potentially conflicting control objectives and safety constraints.
Universal Approximation Theory: Foundations for Parallelism in Neural Networks
Neural networks are increasingly evolving towards training large models with big data, a method that has demonstrated superior performance across many tasks.
Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data
In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data.
DFE-IANet: A Method for Polyp Image Classification Based on Dual-domain Feature Extraction and Interaction Attention
This challenge is mainly attributed to the fact that polyps are similar to other pathologies and have complex features influenced by texture, color, and morphology.
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
Machine Learning has made remarkable progress in a wide range of fields.
Discrete Spectrum Analysis of Vector OFDM Signals
We obtain a linear relationship between a vector of information symbols and a vector of the same size of components evenly distributed in the discrete VOFDM signal spectrum, and show that if a vector of information symbols is set to 0, then a corresponding vector of the same size of the discrete VOFDM signal spectrum is 0 as well, where the components of the 0 vector are not together but evenly distributed in the spectrum.
Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models
We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings.
Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models
The vast amount of biomedical information available today presents a significant challenge for investigators seeking to digest, process, and understand these findings effectively.
Hardware-Efficient and Reliable Coherent DSCM Systems Enabled by Single-Pilot-Tone-Based Polarization Demultiplexing
Besides, a low-complexity, high-spectral-efficiency, and ultra-fast polarization demultiplexing method based on a single pilot tone (SPT) is proposed for the DSCM system in this work.
Pan-cancer Histopathology WSI Pre-training with Position-aware Masked Autoencoder
The results have demonstrated the effectiveness of PAMA in generalized and discriminative WSI representation learning and pan-cancer WSI pre-training.
CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement
Low-Light Image Enhancement (LLIE) has advanced with the surge in phone photography demand, yet many existing methods neglect compression, a crucial concern for resource-constrained phone photography.
Solving General Natural-Language-Description Optimization Problems with Large Language Models
Optimization problems seek to find the best solution to an objective under a set of constraints, and have been widely investigated in real-world applications.
InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
Recent advancements in open-source code large language models (LLMs) have demonstrated remarkable coding abilities by fine-tuning on the data generated from powerful closed-source LLMs such as GPT-3. 5 and GPT-4 for instruction tuning.
CLIMB: A Benchmark of Clinical Bias in Large Language Models
We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in Large Language Models), a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks.
LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
We propose LogicVista, an evaluation benchmark that assesses the integrated logical reasoning capabilities of multimodal large language models (MLLMs) in Visual contexts.
Universal Approximation Theory: The Basic Theory for Deep Learning-Based Computer Vision Models
What is the fundamental difference between residual-based CNNs and Transformer-based networks?
SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules
It commences with our observation that add-on modules, i. e., ControlNets and LoRAs, that augment the base stable diffusion models, are ubiquitous in generating images for commercial applications.
MIRAI: Evaluating LLM Agents for Event Forecasting
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.
Universal Approximation Theory: The Basic Theory for Transformer-based Large Language Models
Language models have emerged as a critical area of focus in artificial intelligence, particularly with the introduction of groundbreaking innovations like ChatGPT.
MVOC: a training-free multiple video object composition method with diffusion models
Although image composition based on diffusion models has been highly successful, it is not straightforward to extend the achievement to video object composition tasks, which not only exhibit corresponding interaction effects but also ensure that the objects in the composited video maintain motion and identity consistency, which is necessary to composite a physical harmony video.
Proving Olympiad Algebraic Inequalities without Human Demonstrations
To address these issues, we propose AIPS, an Algebraic Inequality Proving System capable of autonomously generating complex inequality theorems and effectively solving Olympiad-level inequality problems without requiring human demonstrations.
Ultra-High-Definition Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution
To better utilize these priors, we introduce single prior feature interaction and dual prior feature interaction, where the former respectively fuses normal and gradient priors with high-resolution features to enhance prior ones, while the latter calculates the similarity between enhanced prior ones and further exploits dual guided filtering to boost the feature interaction of dual priors.
AniFaceDiff: High-Fidelity Face Reenactment via Facial Parametric Conditioned Diffusion Models
Face reenactment refers to the process of transferring the pose and facial expressions from a reference (driving) video onto a static facial (source) image while maintaining the original identity of the source image.
Stealth edits for provably fixing or attacking large language models
We reveal new methods and the theoretical foundations of techniques for editing large language models.
Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals
Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application.
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery
In many scientific fields, large language models (LLMs) have revolutionized the way text and other modalities of data (e. g., molecules and proteins) are handled, achieving superior performance in various applications and augmenting the scientific discovery process.
Bayesian Intervention Optimization for Causal Discovery
We demonstrate the effectiveness of our method through various experiments.
Multi-Objective Control Co-design Using Graph-Based Optimization for Offshore Wind Farm Grid Integration
Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact.
CliBench: Multifaceted Evaluation of Large Language Models in Clinical Decisions on Diagnoses, Procedures, Lab Tests Orders and Prescriptions
The integration of Artificial Intelligence (AI), especially Large Language Models (LLMs), into the clinical diagnosis process offers significant potential to improve the efficiency and accessibility of medical care.
Interconnected Markets: Exploring the Dynamic Relationship Between BRICS Stock Markets and Cryptocurrency
This study uses data from the BRICS stock market index, cryptocurrencies, and investor sentiment indicators from January 6, 2015, to June 29, 2023.
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
Furthermore, PFL systems can also deploy both server-end and client-end defense mechanisms to strengthen the barrier against backdoor attacks.
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding.
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method
Solving partial differential equations (PDEs) effectively necessitates a multi-scale approach, particularly critical in high-dimensional scenarios characterized by increasing grid points or resolution.
FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering
We revisit these two components and discover that a part of feature transformation and nonlinear operation during message passing in GCN can improve the representation of GCF, but increase the difficulty of training.
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.
Explore Internal and External Similarity for Single Image Deraining with Graph Neural Networks
To better model this property for image detaining, we develop a multi-scale graph network with exemplars, called MSGNN, that contains two branches: 1) internal data-based supervised branch is used to model the internal relations of similar patches from the rainy image itself and its multi-scale images and 2) external data-participated unsupervised branch is used to model the external relations of the similar patches in the rainy image and exemplar.
Correlation Matching Transformation Transformers for UHD Image Restoration
To better improve feature representation in low-resolution space, we propose to build feature transformation from the high-resolution space to the low-resolution one.
Exploring Channel Estimation and Signal Detection for ODDM-based ISAC Systems
Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication~(ISAC) systems based on the orthogonal delay-Doppler multiplexing~(ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler~(DD) plane.
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data and append its self-generated image descriptions to the prompts.
Ranked #38 on
Visual Question Answering
on MM-Vet
Cross-Context Backdoor Attacks against Graph Prompt Learning
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning.
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
For the learning of positive knowledge, we collect positive rationales through self-consistency to denoise the LLM rationales generated by temperature sampling.
Minimum-Variance Recursive State Estimation for 2-D Systems: When Asynchronous Multi-Channel Delays meet Energy Harvesting Constraints
Based on the reconstructed activated observation sequence and activated probability, a novel unbiased h+1-step recursive estimator is constructed.
Fighter flight trajectory prediction based on spatio-temporal graphcial attention network
The Transformer branch network is used to extract the temporal characteristics of historical trajectories and capture the impact of the fighter's historical state on future trajectories, while the GAT branch network is used to extract spatial features in historical trajectories and capture potential spatial correlations between fighters. Then we concatenate the outputs of the two branches into a new feature vector and input it into a decoder composed of a fully connected network to predict the future position coordinates of the blue army fighter. The computer simulation results show that the proposed network significantly improves the prediction accuracy of flight trajectories compared to the enhanced CNN-LSTM network (ECNN-LSTM), with improvements of 47% and 34% in both ADE and FDE indicators, providing strong support for subsequent autonomous combat missions.
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual context.
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing
Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all.
Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network
Aiming at the problem of low accuracy of flight trajectory prediction caused by the high speed of fighters, the diversity of tactical maneuvers, and the transient nature of situational change in close range air combat, this paper proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction method.
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective
Specifically, we employ an adversarial search strategy to search for the adversarial embedding which can transfer across different unlearned models.
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes.
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates.
No More Ambiguity in 360° Room Layout via Bi-Layout Estimation
A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions.
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness
Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models.
Hierarchical Attention Models for Multi-Relational Graphs
BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2) relation-level attention.
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
We verify the effectiveness of the proposed explanations from two aspects: (1) Counterfactual Trace Visualization: the enhanced forgery images are useful to reveal artifacts by visually contrasting the original images and two different visualization methods; (2) Transferable Adversarial Attacks: the adversarial forgery images generated by attacking the detection model are able to mislead other detection models, implying the removed artifacts are general.
Efficient and Scalable Chinese Vector Font Generation via Component Composition
In this work, we first observe that most Chinese characters can be disassembled into frequently-reused components.
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC).
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
Event Detection from Social Media for Epidemic Prediction
In our work, we pioneer exploiting Event Detection (ED) for better preparedness and early warnings of any upcoming epidemic by developing a framework to extract and analyze epidemic-related events from social media posts.
Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence
Neural rendering techniques have significantly advanced 3D human body modeling.
IterAlign: Iterative Constitutional Alignment of Large Language Models
Such a constitution discovery pipeline can be run iteratively and automatically to discover new constitutions that specifically target the alignment gaps in the current LLM.
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt.
Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures
Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information.
https://paperswithcode.com/paper/negatives-make-a-positive-an-embarrassingly
Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information.
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments.
TablePuppet: A Generic Framework for Relational Federated Learning
In this paper, we formalize this problem as relational federated learning (RFL).
Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks
The radar signal processing algorithm is one of the core components in through-wall radar human detection technology.
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery
Using the Antarctic's Ross Sea region as an example, the U-Net model trained on auto-labeled data achieves a classification accuracy of 98. 97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Memory-Efficient and Secure DNN Inference on TrustZone-enabled Consumer IoT Devices
We design a memory-efficient management method to support memory-demanding inference in TEEs.
Simplified Self-homodyne Coherent System Based on Alamouti Coding and Digital Subcarrier Multiplexing
Coherent technology inherent with more availabledegrees of freedom is deemed a competitive solution for nextgeneration ultra-high-speed short-reach optical interconnects. However, the fatal barriers to implementing the conventiona. coherent system in short-reach optical interconnect are the costfootprint, and power consumption.
TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model
Taking advantage of the fine-tuned language model on scene recognition benchmarks and the paradigm of text block detection, extensive experiments demonstrate the superior performance of our scene text spotter across multiple public benchmarks.
How Powerful Potential of Attention on Image Restoration?
Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
Adaptive Split Learning over Energy-Constrained Wireless Edge Networks
Then, a two-layer optimization method is proposed to solve the MIP problem.
QAQ: Quality Adaptive Quantization for LLM KV Cache
The emergence of LLMs has ignited a fresh surge of breakthroughs in NLP applications, particularly in domains such as question-answering systems and text generation.
Improving Event Definition Following For Zero-Shot Event Detection
We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types.
DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation
Constrained decoding approaches aim to control the meaning or style of text generated by a Pre-trained Language Model (PLM) using specific target words during inference.
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs).
Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion Model with Large Language Models for Machine Translation
Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation.
Generalizing across Temporal Domains with Koopman Operators
By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.
Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks
Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.
TinyLLM: Learning a Small Student from Multiple Large Language Models
To solve the problems and facilitate the learning of compact language models, we propose TinyLLM, a new knowledge distillation paradigm to learn a small student LLM from multiple large teacher LLMs.
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare
This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios.
Polyp-DAM: Polyp segmentation via depth anything model
In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models.
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
Recently, SSL has become a new trend in exploring the representation learning capability in the realm of tabular data, which is more challenging due to not having explicit relations for learning descriptive representations.
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation
The previous methods either failed to accurately fit the face region or lost the interactive generative ability with other existing concepts in T2I models.
Spatial Computing: Concept, Applications, Challenges and Future Directions
In this review, we also explored the potential challenges and limitations of spatial computing.
On the Emergence of Symmetrical Reality
Artificial intelligence (AI) has revolutionized human cognitive abilities and facilitated the development of new AI entities capable of interacting with humans in both physical and virtual environments.
Emulation-based Stabilization for Networked Control Systems with Stochastic Channels
Next, the proposed scheduling protocol is embedded into the closed-loop system, which leads to a stochastic hybrid model for NCSs with random packet dropouts.
Online Mean Estimation for Multi-frame Optical Fiber Signals On Highways
In the era of Big Data, prompt analysis and processing of data sets is critical.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
ConcEPT: Concept-Enhanced Pre-Training for Language Models
In this paper, we propose ConcEPT, which stands for Concept-Enhanced Pre-Training for language models, to infuse conceptual knowledge into PLMs.
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks.