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Found 13 papers, 6 papers with code
MLP Can Be A Good Transformer Learner
We identify that regarding the attention layer in bottom blocks, their subsequent MLP layers, i. e. two feed-forward layers, can elicit the same entropy quantity.
Self-Supervised Multi-Frame Neural Scene Flow
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field.
Identifying Semantic Induction Heads to Understand In-Context Learning
Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness.
Unified Batch Normalization: Identifying and Alleviating the Feature Condensation in Batch Normalization and a Unified Framework
In this work, we critically examine BN from a feature perspective, identifying feature condensation during BN as a detrimental factor to test performance.
Concept-Level Explanation for the Generalization of a DNN
Therefore, in this paper, we investigate the generalization power of each interactive concept, and we use the generalization power of different interactive concepts to explain the generalization power of the entire DNN.
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors
In this paper, we propose to jointly use both global and local descriptors to register point clouds in a self-supervised manner, which is motivated by a critical observation that all local geometries of point clouds are transformed consistently under the same transformation.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
Trap of Feature Diversity in the Learning of MLPs
In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity in the first phase.
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing.
Deep Models with Fusion Strategies for MVP Point Cloud Registration
The main goal of point cloud registration in Multi-View Partial (MVP) Challenge 2021 is to estimate a rigid transformation to align a point cloud pair.
Point Cloud Registration using Representative Overlapping Points
3D point cloud registration is a fundamental task in robotics and computer vision.
A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation
We prove that CAT is a rotation and translation-invariant transformation based on the theoretical analysis.
