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Found 46 papers, 20 papers with code
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names.
Data-driven Internal Model Control for Output Regulation
Extensions to both linear and nonlinear MASs are discussed.
Impact of Grid-Forming Inverters on Protective Relays: A Perspective for Current Limiting Control Design
It is further found that even with a highly inductive virtual impedance, the altered virtual impedance dynamics introduced by the CLC can still lead to malfunctions of the incremental quantity-based supervising elements.
A Protection-Interoperable Fault Ride-Through Control for Grid-Forming Inverters
This method dynamically adjusts power control of GFM-IBRs in response to the changes in output impedance, effectively mitigating variations in source dynamics and thereby preserving the reliability of incremental quantity-based supervising elements.
Analysis of Power Swing Characteristics of Grid-Forming VSC System Considering the Current Limitation Mode
This paper investigates power swing characteristics of grid-forming voltage source converter (GFM-VSC) systems considering the current limitation mode in both non-inertial and inertial GFM-VSC systems.
Wan: Open and Advanced Large-Scale Video Generative Models
Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community.
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers
Neural solvers have shown significant potential in solving the Traveling Salesman Problem (TSP), yet current approaches face significant challenges.
Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms
In recent years, machine learning algorithms have achieved much success in segmenting lesions across various tissues.
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding
We evaluate 16 leading models on MedXpertQA.
Static Batching of Irregular Workloads on GPUs: Framework and Application to Efficient MoE Model Inference
It has long been a problem to arrange and execute irregular workloads on massively parallel devices.
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
OVO-Bench evaluates the ability of video LLMs to reason and respond to events occurring at specific timestamps under three distinct scenarios: (1) Backward tracing: trace back to past events to answer the question.
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions
Recent advancements in multimodal large language models (MLLMs) have made significant strides in open-world understanding.
Timestamp calibration for time-series single cell RNA-seq expression data
To tackle this challenge, we have developed a novel timestamp calibration model called ScPace for handling noisy labeled time-series ScRNA-seq data.
Lattice-based stochastic models motivate non-linear diffusion descriptions of memory-based dispersal
The advantage of working with the individual-based model is that it is straightforward to propose and implement memory effects within the simulation in a way that is more biologically intuitive than simply proposing heuristic extensions of classical PDE models.
Automating Exploratory Proteomics Research via Language Models
By automating complex proteomics analysis workflows and hypothesis generation, PROTEUS has the potential to considerably accelerate the pace of scientific discovery in proteomics research, enabling researchers to efficiently explore large-scale datasets and uncover biological insights.
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin
FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery.
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control
The firstly developed algorithm is named as Deep Deterministic Policy Gradient with Symmetric Data Augmentation (DDPG-SDA), which enriches dataset of standard DDPG algorithm by symmetric data augmentation method under symmetry assumption of a dynamical system.
Learning Physical Simulation with Message Passing Transformer
We propose a new universal architecture based on Graph Neural Network, the Message Passing Transformer, which incorporates a Message Passing framework, employs an Encoder-Processor-Decoder structure, and applies Graph Fourier Loss as loss function for model optimization.
NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation
We present a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries.
TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation
To address this issue, we introduce TheaterGen, a training-free framework that integrates large language models (LLMs) and text-to-image (T2I) models to provide the capability of multi-turn image generation.
Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields
In this paper, we propose a novel low bandwidth neural compression approach for high-fidelity portrait video conferencing using implicit radiance fields to achieve both major objectives.
AttributionBench: How Hard is Automatic Attribution Evaluation?
Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence.
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions.
Ranked #24 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation
By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body, our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way.
Robust Control of Unknown Switched Linear Systems from Noisy Data
This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics.
TableLlama: Towards Open Large Generalist Models for Tables
Towards that end, we construct TableInstruct, a new dataset with a variety of realistic tables and tasks, for instruction tuning and evaluating LLMs.
Efficacy Analysis of Power Swing Blocking and Out-of-Step Tripping Functions in Grid-Following VSC Systems
Power flow oscillations can occur in power systems after major disturbances such as system faults, which may result in significant power swings and even lead to system collapse.
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation
We propose both serial and parallel proximal (linearized) alternating direction method of multipliers (ADMM) algorithms for training residual neural networks.
Self-triggered Consensus Control of Multi-agent Systems from Data
In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC.
Flows: Building Blocks of Reasoning and Collaborating AI
To support rapid and rigorous research, we introduce the aiFlows library embodying Flows.
How Can Large Language Models Help Humans in Design and Manufacturing?
The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design.
Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data
This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization.
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
To better break down the problem, we define a systematic framework with three components: prompting strategies for eliciting verbalized confidence, sampling methods for generating multiple responses, and aggregation techniques for computing consistency.
A Method for Animating Children's Drawings of the Human Figure
Children's drawings have a wonderful inventiveness, creativity, and variety to them.
Neural Characteristic Function Learning for Conditional Image Generation
The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information.
A Survey on Knowledge-Enhanced Pre-trained Language Models
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT.
Making Large Language Models Better Reasoners with Step-Aware Verifier
Few-shot learning is a challenging task that requires language models to generalize from limited examples.
Ranked #54 on
Arithmetic Reasoning
on GSM8K
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.
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method
E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start.
Ranked #3 on
Saliency Prediction
on SALECI
Interpreting how nonlinear diffusion affects the fate of bistable populations using a discrete modelling framework
One way of exploring this question is to study population dynamics using reaction-diffusion equations, where migration is usually represented as a linear diffusion term, and birth-death is represented with a bistable source term.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact
This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications.
Extinction of bistable populations is affected by the shape of their initial spatial distribution
In this work we study population survival or extinction using a stochastic, discrete lattice-based random walk model where individuals undergo movement, birth and death events.
An energy-stable parametric finite element method for anisotropic surface diffusion
We propose an energy-stable parametric finite element method (ES-PFEM) to discretize the motion of a closed curve under surface diffusion with an anisotropic surface energy $\gamma(\theta)$ -- anisotropic surface diffusion -- in two dimensions, while $\theta$ is the angle between the outward unit normal vector and the vertical axis.
Numerical Analysis Numerical Analysis
FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification
This paper has proposed a new baseline deep learning model of more benefits for image classification.
