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Found 14 papers, 2 papers with code
Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs
This work focuses on generating realistic, physically-based human behaviors from multi-modal inputs, which may only partially specify the desired motion.
AHSG: Adversarial Attacks on High-level Semantics in Graph Neural Networks
To address this problem, we propose a Adversarial Attacks on High-level Semantics in Graph Neural Networks (AHSG), which is a graph structure attack model that ensures the retention of primary semantics.
Can Large Language Models Logically Predict Myocardial Infarction? Evaluation based on UK Biobank Cohort
Future medical LLMs are suggested to be expert in medical domain knowledge to understand both natural languages and quantified medical data, and further make logical inferences.
Cooperative Multi-Agent Deep Reinforcement Learning in Content Ranking Optimization
In a typical e-commerce setting, Content Ranking Optimization (CRO) mechanisms are employed to surface content on the search page to fulfill customers' shopping missions.
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots.
Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs
We compare our approach to a similar CUDA implementation for MLPs and show that our implementation on the Intel Data Center GPU outperforms the CUDA implementation on Nvidia's H100 GPU by a factor up to 2. 84 in inference and 1. 75 in training.
Hierarchical generative modelling for autonomous robots
We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control.
Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations
To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable.
Deep Pairwise Learning To Rank For Search Autocomplete
Autocomplete (a. k. a "Query Auto-Completion", "AC") suggests full queries based on a prefix typed by customer.
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations.
Learning Pregrasp Manipulation of Objects from Ungraspable Poses
In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped from the side.
Robotics
Reaching, Grasping and Re-grasping: Learning Fine Coordinated Motor Skills
The performance of learned policy is evaluated on three different tasks: grasping a static target, grasping a dynamic target, and re-grasping.
Robotics
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
Force-guided High-precision Grasping Control of Fragile and Deformable Objects using sEMG-based Force Prediction
Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects.
Robotics
