Search Results for author:
Found 23 papers, 15 papers with code
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis
Our model also achieves state-of-the-art performance on a widely used sarcasm dataset.
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches.
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
Automated red teaming is an effective method for identifying misaligned behaviors in large language models (LLMs).
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation
To address the above challenges, we propose a novel multi-modal FL framework for brain tumor segmentation (Fed-MUnet) that is suitable for FL training.
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?"
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polarities from the given text-image pairs, has gained increasing concerns.
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability.
MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare.
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems
The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.
DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance.
Optimizing Interactive Systems via Data-Driven Objectives
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
An Additive Decomposition in S-Primitive Towers
We consider the additive decomposition problem in primitive towers and present an algorithm to decompose a function in an S-primitive tower as a sum of a derivative in the tower and a remainder which is minimal in some sense.
Symbolic Computation
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.
Learning Data-Driven Objectives to Optimize Interactive Systems
Effective optimization is essential for interactive systems to provide a satisfactory user experience.
Additive Decompositions in Primitive Extensions
This paper extends the classical Ostrogradsky-Hermite reduction for rational functions to more general functions in primitive extensions of certain types.
Symbolic Computation 33F10, 68W30, 12H05
Hermite Reduction and Creative Telescoping for Hyperexponential Functions
Based on this reduction algorithm, we design a new method to compute minimal telescopers for bivariate hyperexponential functions.
Symbolic Computation Combinatorics 33F10
