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Found 9 papers, 4 papers with code
Incremental Sequence Labeling: A Tale of Two Shifts
As for the E2O problem, we use knowledge distillation to maintain the model's discriminative ability for old entities.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
H-GAP: Humanoid Control with a Generalist Planner
However, the extensive collection of human motion-captured data and the derived datasets of humanoid trajectories, such as MoCapAct, paves the way to tackle these challenges.
ChessGPT: Bridging Policy Learning and Language Modeling
Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
While offline RL algorithms can in principle be used for finetuning, in practice, their online performance improves slowly.
Optimal Transport for Offline Imitation Learning
In this paper, we introduce Optimal Transport Reward labeling (OTR), an algorithm that assigns rewards to offline trajectories, with a few high-quality demonstrations.
Learning to Construct 3D Building Wireframes from 3D Line Clouds
The network takes a line cloud as input , i. e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments.
ELSD: Efficient Line Segment Detector and Descriptor
The line description ability of ELSD also outperforms the previous works on the line matching task.
Ranked #1 on
Line Segment Detection
on wireframe dataset
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itself using probabilistic programming constructs.
