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Found 27 papers, 10 papers with code
TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention
Existing sparse attention mechanisms designed to address this bottleneck have two limitations: (1) they often fail to reliably identify the most relevant tokens for attention, and (2) they overlook the spatial coherence of token selection across consecutive Transformer layers, which can lead to performance degradation and substantial overhead in token selection.
ATLAS: Improving Lay Summarisation with Attribute-based Control
Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences.
RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection
However, due to a lack of aligned multimodal datasets, effective and robust methods for detecting machine-generated content are still in the early stages of development.
Game-MUG: Multimodal Oriented Game Situation Understanding and Commentary Generation Dataset
In addition, we also propose a new audience conversation augmented commentary dataset by covering the game situation and audience conversation understanding, and introducing a robust joint multimodal dual learning model as a baseline.
Adaptive Learning for Multi-view Stereo Reconstruction
Deep learning has recently demonstrated its excellent performance on the task of multi-view stereo (MVS).
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding
The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes.
Ranked #1 on
Zero-shot 3D Point Cloud Classification
on ScanObjectNN (Pretrained on ShapeNet)
(using extra training data)
Unveiling Linguistic Regions in Large Language Models
Furthermore, this core region exhibits significant dimensional dependence, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence.
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.
Accelerating Retrieval-Augmented Language Model Serving with Speculation
Retrieval-augmented language models (RaLM) have demonstrated the potential to solve knowledge-intensive natural language processing (NLP) tasks by combining a non-parametric knowledge base with a parametric language model.
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.
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
Finally, we merge the quality phrases from both the Annotator and Generator as the final predictions, considering their complementary nature and distinct characteristics.
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
In the rapidly evolving landscape of artificial intelligence (AI), generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data.
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e. g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience.
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.
Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer
Viewport prediction is a crucial aspect of tile-based 360 video streaming system.
SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification
Our evaluation shows that SpecInfer outperforms existing LLM serving systems by 1. 5-2. 8x for distributed LLM inference and by 2. 6-3. 5x for offloading-based LLM inference, while preserving the same generative performance.
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
AbHE: All Attention-based Homography Estimation
While the state-of-theart homography method is based on convolution neural networks, few work focuses on transformer which shows superiority in highlevel vision tasks.
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily.
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse.
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence.
NGEP: A Graph-based Event Planning Framework for Story Generation
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i. e. storylines) to guide story generation.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.
Ranked #1 on
Lay Summarization
on PLOS
Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks
To address the limitations of existing quantum ML methods, we introduce Quark, a gradient-free quantum learning framework that optimizes quantum ML models using quantum optimization.
GradSign: Model Performance Inference with Theoretical Insights
In addition, we design GradSign, an accurate and simple approximation of {\Psi} using the gradients of a network evaluated at a random initialization state.
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios.
