Search Results for author:
Found 27 papers, 16 papers with code
LLM-DSE: Searching Accelerator Parameters with LLM Agents
Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle.
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline.
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Notably, at 50% pruning, SEAP surpasses both WandA and FLAP by over 20%, and at 20% pruning, it incurs only a 2. 2% performance drop compared to the dense model.
Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants
For organisms with large, similar genomes like Escherichia coli and Shigella flexneri, differentiating between species is also difficult but crucial.
SurveyX: Academic Survey Automation via Large Language Models
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation.
TruthFlow: Truthful LLM Generation via Representation Flow Correction
Large language models (LLMs) are known to struggle with consistently generating truthful responses.
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
However, the incorporation of external and unverified knowledge increases the vulnerability of LLMs because attackers can perform attack tasks by manipulating knowledge.
WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in the Spatial-Frequency Domain
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment.
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
By incorporating the prior model during training, LARP learns a latent space that is not only optimized for video reconstruction but is also structured in a way that is more conducive to autoregressive generation.
Ranked #3 on
Video Generation
on UCF-101
SaVe-TAG: Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes.
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations.
Controllable Text Generation for Large Language Models: A Survey
This paper systematically reviews the latest advancements in CTG for LLMs, offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality.
Internal Consistency and Self-Feedback in Large Language Models: A Survey
In this paper, we use a unified perspective of internal consistency, offering explanations for reasoning deficiencies and hallucinations.
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and Explainability
HealthRCN is the largest known dataset of Chinese health information rumors to date.
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs).
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images.
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w. r. t.
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
Deep learning forward and reverse primer design to detect SARS-CoV-2 emerging variants
Hence, in this paper, we develop a semi-automated method to design both forward and reverse primer sets to detect SARS-CoV-2 variants.
Neural Space-filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
NeRV: Neural Representations for Videos
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Ranked #7 on
Video Reconstruction
on UVG
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
