Genre
Just Add $ 100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem
PGT -Aug involves three key steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity simulation, and (iii) a hybrid context-aware placement method from ground and map information. We demonstrate the superiority and generality of our method through performance improvements in extensive experiments conducted on popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the datasets with large domain gaps
Noether Embedding: Efficient Learning of Temporal Regularities Chi Gao
Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections Chun-Han Y ao 1 * Amit Raj 2 Wei-Chih Hung 3 Y uanzhen Li2
Specifically, ARTIC3D is built upon a skeleton-based surface representation and is further guided by 2D diffusion priors from Stable Diffusion. First, we enhance the input images with occlusions/truncation via 2D diffusion to obtain cleaner mask estimates and semantic features.
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. However, existing theoretical studies on AIL are primarily limited to simplified scenarios such as tabular and linear function approximation and involve complex algorithmic designs that hinder practical implementation, highlighting a gap between theory and practice.
Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA
Foundation models have made significant strides in understanding the genomic language of DNA sequences. However, previous models typically adopt the tok-enization methods designed for natural language, which are unsuitable for DNA sequences due to their unique characteristics. In addition, the optimal approach to tokenize DNA remains largely under-explored, and may not be intuitively understood by humans even if discovered. To address these challenges, we introduce MxDNA, a novel framework where the model autonomously learns an effective DNA tokenization strategy through gradient decent.