Search Results for author:
Found 15 papers, 5 papers with code
"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
On the other side are perhaps over-pessimistic claims that all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another, and ship the problem off to external symbolic solvers.
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost.
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences
We propose two practical methods that can learn to model any kind of behavioral attributes from ordered behavior clips.
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
If the relevant concept is not in the shared vocabulary, then it is learned.
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
At the low level, we learn a set of diverse policies for each possible task subgoal identified by the landmark, which are then stitched together.
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) has gained significant popularity since it relies only on weak labels such as image level annotations rather than pixel level annotations required by supervised semantic segmentation (SSS) methods.
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
Inspired by gradient-based neural architecture search (NAS) and network pruning methods, people have tackled the NFS problem with Gating approach that inserts a set of differentiable binary gates to drop less informative features.
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of traditional RLfD works.
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities.
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping
In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
QoS-aware Link Scheduling Strategy for Data Transmission in SDVN
The original contributions of this paper are summarized as follows: (1) Model the packets collision probability of broadcast or NACK transmission in VANET with the combination theory and investigate the potential influence of miss my packets (MMP) problem.
Networking and Internet Architecture
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
We focus on the task of learning from feedback, in which the human trainer not only gives binary evaluative "good" or "bad" feedback for queried state-action pairs, but also provides a visual explanation by annotating relevant features in images.
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
