Search Results for author:
Found 20 papers, 5 papers with code
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
Interpretable Contrastive Monte Carlo Tree Search Reasoning
Thus, we conducted extensive ablation studies and quantitative analysis on components of MCTS, revealing the impact of each component on the MCTS reasoning performance of LLMs.
Inference for Large Scale Regression Models with Dependent Errors
While machine learning has scaled to achieve predictive accuracy in big data settings, statistical inference and uncertainty quantification tools are still lagging.
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
Although Large Language Models (LLMs) have demonstrated strong instruction-following ability, they are further supposed to be controlled and guided by rules in real-world scenarios to be safe, accurate, and intelligent.
Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models
For class-level effectiveness, we design a two-branch generation structure that consists of a Diffusion-based Feature Generator (DFG) and a Diffusion-based Representation Generator (DRG).
Ranked #1 on
Zero-Shot Learning
on AwA2
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
We propose a full asynchronous training paradigm, called FedFa, which can guarantee model convergence and eliminate the waiting time completely for federated learning by using a few buffered results on the server for parameter updating.
EMONA: Event-level Moral Opinions in News Articles
This paper initiates a new task to understand moral opinions towards events in news articles.
Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
Many types of NNs (such as Recurrent Neural Network (RNN), and transformers) that are designed to work with sequential data have advantages over tree-based models, because they can take raw network data as input without complex feature computations on the fly.
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach
In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance.
DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands
Achieving human-like dexterous manipulation remains a crucial area of research in robotics.
No Train Still Gain. Unleash Mathematical Reasoning of Large Language Models with Monte Carlo Tree Search Guided by Energy Function
Large language models (LLMs) demonstrate impressive language understanding and contextual learning abilities, making them suitable for natural language processing (NLP) tasks and complex mathematical reasoning.
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization
In the early stage, our method loosens the practical constraints of unsafe transitions (adding extra safety budget) with the aid of a new metric we propose.
A Policy Optimization Method Towards Optimal-time Stability
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization.
VizInspect Pro -- Automated Optical Inspection (AOI) solution
Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregated insights.
Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works
Event Extraction bridges the gap between text and event signals.
Directionally Convolutional Networks for 3D Shape Segmentation
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors.
Joint Bayesian Gaussian discriminant analysis for speaker verification
State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis.
Joint Stochastic Approximation learning of Helmholtz Machines
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables.
