Search Results for author:
Found 27 papers, 13 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.
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 #13 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 Training Algorithms for Residual Networks: Convergence, Complexity and Parallel Training
Convergence of the proximal point version is proven based on a Kurdyka-Lojasiewicz (KL) property analysis framework, and we can ensure a locally R-linear or sublinear convergence rate depending on the different ranges of the Kurdyka-Lojasiewicz (KL) exponent, in which a necessary auxiliary function is constructed to realize our goal.
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.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.
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 #49 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 #2 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.
