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Found 223 papers, 94 papers with code
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Augmenting Topic Aware Knowledge-Grounded Conversations with Dynamic Built Knowledge Graphs
Dialog topic management and background knowledge selection are essential factors for the success of knowledge-grounded open-domain conversations.
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning
Generating informative and appropriate responses is challenging but important for building human-like dialogue systems.
Improving Constituent Representation with Hypertree Neural Networks
Many natural language processing tasks involve text spans and thus high-quality span representations are needed to enhance neural approaches to these tasks.
Dispersed EM-VAEs for Interpretable Text Generation
Interpretability is important in text generation for guiding the generation with interpretable attributes.
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases.
Structure-Based Molecule Optimization via Gradient-Guided Bayesian Update
Structure-based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets.
MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
To address these issues, we present MutaPLM, a unified framework for interpreting and navigating protein mutations with protein language models.
P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics
The model consists of two synergistic modules: (1) a trainable PDE block that learns to update the coarse solution (i. e., the system state), based on a high-order numerical scheme with boundary condition encoding, and (2) a neural network block that consistently corrects the solution on the fly.
MiniPLM: Knowledge Distillation for Pre-Training Language Models
For effectiveness, MiniPLM leverages the differences between large and small LMs to enhance the difficulty and diversity of the training data, helping student LMs acquire versatile and sophisticated knowledge.
MobA: A Two-Level Agent System for Efficient Mobile Task Automation
Current mobile assistants are limited by dependence on system APIs or struggle with complex user instructions and diverse interfaces due to restricted comprehension and decision-making abilities.
A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks
As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, their deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance.
Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving
In particular, for LLM-enabled AD, we employ the Euclidean distance to identify previously explored AD experiences, and then LLMs can learn from past good and bad decisions for further improvement.
Numerical analysis of American option pricing in a two-asset jump-diffusion model
Existing numerical methods, primarily based on finite differences, often struggle with preserving monotonicity in the approximation of cross-derivatives-a key requirement for ensuring convergence to the viscosity solution.
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
We compare the performance of the same optimization methods across different hardware platforms, the performance across different hardware platforms, and the performance of different methods on the same hardware platform.
SLAM assisted 3D tracking system for laparoscopic surgery
The primitive 3D shape is used for fast initialization of the monocular SLAM.
Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint
Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields remains a challenge.
MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing
Then, the model reviews the knowledge of the original languages with replay data amounting to less than 1% of post-pretraining, where we incorporate language priors routing to better recover the abilities of the original languages.
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
Following this comprehensive feedback, our proposed TrafficLLM introduces refinement demonstration prompts, enabling the same LLM to further refine its predictions and thereby enhance prediction performance.
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
In particular, we first introduce the communication system model, i. e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation.
Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
It avoids the complexity of tedious model training and hyper-parameter fine-tuning, which is a well-known bottleneck of many ML algorithms.
STATE: A Robust ATE Estimator of Heavy-Tailed Metrics for Variance Reduction in Online Controlled Experiments
In this paper, we develop a novel framework that integrates the Student's t-distribution with machine learning tools to fit heavy-tailed metrics and construct a robust average treatment effect estimator in online controlled experiments, which we call STATE.
I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction
Visual Language Models (VLMs) are essential for various tasks, particularly visual reasoning tasks, due to their robust multi-modal information integration, visual reasoning capabilities, and contextual awareness.
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
In this paper, we investigate the use of diffusion models which are pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic understanding.
Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization
Decision Focused Learning (DFL) integrates ML and OR into an end-to-end framework, which takes the objective of the downstream task as the decision loss function and guarantees the consistency of the optimization direction between ML and OR.
SGSM: A Foundation-model-like Semi-generalist Sensing Model
The significance of intelligent sensing systems is growing in the realm of smart services.
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs.
Skim then Focus: Integrating Contextual and Fine-grained Views for Repetitive Action Counting
The key to action counting is accurately locating each video's repetitive actions.
Semi-Supervised Spoken Language Glossification
Spoken language glossification (SLG) aims to translate the spoken language text into the sign language gloss, i. e., a written record of sign language.
StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond
Text-rich images have significant and extensive value, deeply integrated into various aspects of human life.
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain.
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire.
Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest
We further condition SSM's global predictions on the accumulation of local results.
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Then, we introduce LLM-enabled key techniques and telecom applications in terms of generation, classification, optimization, and prediction problems.
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments.
Collage: Light-Weight Low-Precision Strategy for LLM Training
Large models training is plagued by the intense compute cost and limited hardware memory.
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation
Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, e. g., text-conditioned image editing, allowing us to edit the diverse images that convey highly complex visual concepts according to the textual guidance.
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
In this paper, we showcase HLAT: a family of 7B and 70B decoder-only LLMs pre-trained using 4096 AWS Trainium accelerators over 1. 8 trillion tokens.
The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
Few-shot learning aims to further enhance the transfer capability of CLIP by giving few images in each class, aka 'few shots'.
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Evaluated across a range of both masked and autoregressive LMs on benchmark GLUE tasks, MeZO-SVRG outperforms MeZO with up to 20% increase in test accuracies in both full- and partial-parameter fine-tuning settings.
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks.
Ranked #158 on
Visual Question Answering
on MM-Vet
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i. e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling.
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
Agents share Q-value network periodically during the training process.
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness.
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling
We develop a novel 3D graph editing model to generate molecules using fragments, and pre-train this model on abundant 3D ligands for learning target-independent properties.
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
A monotone piecewise constant control integration approach for the two-factor uncertain volatility model
Numerical results show remarkable agreement with benchmark solutions obtained by unconditionally monotone finite differences, tree methods, and Monte Carlo simulation, underscoring the robustness and effectiveness of our method.
FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition
To address these issues, FROSTER employs a residual feature distillation approach to ensure that CLIP retains its generalization capability while effectively adapting to the action recognition task.
Revisiting the Markov Property for Machine Translation
In this paper, we re-examine the Markov property in the context of neural machine translation.
Multi-fidelity physics constrained neural networks for dynamical systems
As a result, during the training of predictive models, physical constraints can be evaluated within low-fidelity spaces, yielding a trade-off between training efficiency and accuracy.
On Prompt-Driven Safeguarding for Large Language Models
In this work, we investigate how LLMs' behavior (i. e., complying with or refusing user queries) is affected by safety prompts from the perspective of model representation.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Visual Question Answering (VQA)
on InfographicVQA
Equivariant Flow Matching with Hybrid Probability Transport
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Our key innovation is to develop a multi-stage diffusion process.
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion
We benchmarked the proposed evaluation metrics on 12 open-vocabulary methods of three segmentation tasks.
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image.
Large Language Models Are Not Robust Multiple Choice Selectors
This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent "selection bias", namely, they prefer to select specific option IDs as answers (like "Option A").
TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching
Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions.
Sign Language Translation with Iterative Prototype
Technically, IP-SLT consists of feature extraction, prototype initialization, and iterative prototype refinement.
Ranked #6 on
Sign Language Translation
on CSL-Daily
A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy
This requirement is a prerequisite for obtaining a building permit during the conceptual design of a residential project.
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs.
Multimodal Molecular Pretraining via Modality Blending
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate the video understanding capabilities of existing foundation models (FMs) using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring an FM for downstream tasks.
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification
It uses granular balls to simulate the spatial distribution characteristics of datasets, and informed entropy is utilized to further optimize the granular-ball space.
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions
Secondly, we find that LLMs' ability to carry out translation instructions relies on the understanding of translation instructions and the alignment among different languages.
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
Non-Autoregressive Document-Level Machine Translation
However, current NAT models still have a significant performance gap compared to their AT counterparts.
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning
In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes.
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions.
HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction.
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery.
Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training
However, the pre-trained dialogue model's ability to utilize long-range context is limited due to the scarcity of long-turn dialogue sessions.
Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep and Power Control in RIS-aided Energy-Efficient RAN
Then, we introduce a fractional programming method for RIS phase-shift control, maximizing the sum-rate under a given transmission power.
Learning Harmonic Molecular Representations on Riemannian Manifold
In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques.
Towards Diverse Temporal Grounding under Single Positive Labels
In this study, we reformulate this task as a one-vs-many optimization problem under the condition of single positive labels.
Cross-modal information fusion for voice spoofing detection
Researchers have proposed many methods to defend against these attacks, but in the existing methods, researchers just focus on speech features.
Ranked #1 on
Voice Anti-spoofing
on ASVspoof 2019 - PA
Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning
Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test and is closer to the maximum theoretical EE.
On Pre-trained Language Models for Antibody
The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks.
Graph Contrastive Learning for Skeleton-based Action Recognition
In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences.
Ranked #13 on
Skeleton Based Action Recognition
on NTU RGB+D
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner.
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN
Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks.
Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning
Previously, non-autoregressive models were widely perceived as being superior in generation efficiency but inferior in generation quality due to the difficulties of modeling multiple target modalities.
Accelerating Antimicrobial Peptide Discovery with Latent Structure
By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures.
Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing
Our key intuition is that we introduce the decision factor to establish a bridge between ML and OR such that the solution can be directly obtained in OR by only performing the sorting or comparison operations on the decision factor.
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks.
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition
Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis.
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.