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Found 34 papers, 15 papers with code
Cosmos World Foundation Model Platform for Physical AI
We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications.
SAM-REF: Rethinking Image-Prompt Synergy for Refinement in Segment Anything
Experimentally, we demonstrated the high effectiveness and efficiency of our method in tackling complex cases with multiple interactions.
Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models
This study exams whether Large Language Models (LLMs) can predict the Big Five personality traits directly from counseling dialogues and introduces an innovative framework to perform the task.
Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities and scenarios.
SGRU: A High-Performance Structured Gated Recurrent Unit for Traffic Flow Prediction
Traffic flow prediction is an essential task in constructing smart cities and is a typical Multivariate Time Series (MTS) Problem.
Adversarially-Robust Inference on Trees via Belief Propagation
We first confirm a folklore belief that a malicious adversary who can corrupt an inverse-polynomial fraction of the leaves of their choosing makes this inference impossible.
Facilitating Pornographic Text Detection for Open-Domain Dialogue Systems via Knowledge Distillation of Large Language Models
Pornographic content occurring in human-machine interaction dialogues can cause severe side effects for users in open-domain dialogue systems.
Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs
Through further LLM-based evaluations on additional conversations, our findings underscore the challenges counselors face in cultivating strong online relationships with clients.
General Method for Solving Four Types of SAT Problems
We experimentally verify that this paradigm can prune the unnecessary search space to find the optimal Boolean assignments for the problem.
PsyChat: A Client-Centric Dialogue System for Mental Health Support
Dialogue systems are increasingly integrated into mental health support to help clients facilitate exploration, gain insight, take action, and ultimately heal themselves.
ConceptPsy:A Benchmark Suite with Conceptual Comprehensiveness in Psychology
The critical field of psychology necessitates a comprehensive benchmark to enhance the evaluation and development of domain-specific Large Language Models (LLMs).
Facilitating NSFW Text Detection in Open-Domain Dialogue Systems via Knowledge Distillation
NSFW (Not Safe for Work) content, in the context of a dialogue, can have severe side effects on users in open-domain dialogue systems.
Learning Source Biases: Multisource Misspecifications and Their Impact on Predictions
We study how a Bayesian decision maker (DM) learns about the biases of novel information sources to predict a random state.
A Benchmark for Understanding Dialogue Safety in Mental Health Support
Our study reveals that ChatGPT struggles to detect safety categories with detailed safety definitions in a zero- and few-shot paradigm, whereas the fine-tuned model proves to be more suitable.
SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark
We fill this gap by proposing a comprehensive Chinese benchmark SuperCLUE, named after another popular Chinese LLM benchmark CLUE.
Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output Robustness of Large Language Models
We present a systematic analysis of the safety and robustness of LLMs regarding the position of explicit normal instructions, word replacements (verbs in explicit normal instructions, target groups in malicious instructions, cue words for explicit normal instructions), and instruction replacements (different explicit normal instructions).
You Can Mask More For Extremely Low-Bitrate Image Compression
Extensive experiments have demonstrated that our approach outperforms recent state-of-the-art methods in R-D performance, visual quality, and downstream applications, at very low bitrates.
Understanding Client Reactions in Online Mental Health Counseling
Communication success relies heavily on reading participants' reactions.
NICE-SLAM with Adaptive Feature Grids
NICE-SLAM is a dense visual SLAM system that combines the advantages of neural implicit representations and hierarchical grid-based scene representation.
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
Further, we employ our method to generate a large-scale, lifelike, and diverse dialogue dataset named SMILECHAT, consisting of 55k dialogues.
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
We study a new paradigm for sequential decision making, called offline policy learning from observations (PLfO).
Crop mapping in the small sample/no sample case: an approach using a two-level cascade classifier and integrating domain knowledge
To solve this problem, a crop mapping method in the small sample/no sample case that integrating domain knowledge and using a cascaded classification framework that combine a weak classifier learned from samples with strong features and a strong classifier trained by samples with weak feature was proposed.
Rationally Inattentive Statistical Discrimination: Arrow Meets Phelps
When information acquisition is costly but flexible, a principal may rationally acquire information that favors one group over another.
Towards Automated Real-time Evaluation in Text-based Counseling
However, these methods often demand large scale and high quality counseling data, which are difficult to collect.
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Reactive motion generation problems are usually solved by computing actions as a sum of policies.
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distributional shift by interacting with oracular experts.
A Rational Inattention Theory of Echo Chamber
We develop a rational inattention theory of echo chambers, where players allocate limited attention across biased primary sources and other players to gather information about an uncertain state.
RMP2: A Structured Composable Policy Class for Robot Learning
Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots.
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned.
Electoral Accountability and Selection with Personalized Information Aggregation
We study a model of electoral accountability and selection whereby heterogeneous voters aggregate incumbent politician's performance data into personalized signals through paying limited attention.
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
The complex motions are encoded as rollouts of a stable dynamical system, which, under a change of coordinates defined by a diffeomorphism, is equivalent to a simple, hand-specified dynamical system.
The Politics of Personalized News Aggregation
We study how personalized news aggregation for rational inattentive voters (NARI) affects policy polarization and public opinion.
Propaganda, Alternative Media, and Accountability in Fragile Democracies
In addition to regular high- and low-competence types, the incumbent may be an aspiring autocrat who controls the mainstream media and will subvert democracy if retained in office.
Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies
We propose a collection of RMPs for simple multi-robot tasks that can be used for building controllers for more complicated tasks.
Robotics
