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Found 7 papers, 2 papers with code
Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model.
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives.
AffineQuant: Affine Transformation Quantization for Large Language Models
Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of considerable interest due to its noteworthy compression efficiency and cost-effectiveness in the context of training.
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition
Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field.
Ranked #12 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Learning to swim in potential flow
To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control.
Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network
This letter proposes a method to directly perform cloud detection in Landsat-8 imagery in GEE based on deep learning (DeepGEE-CD).
A Method to Support Difficult Re-finding Tasks
To alleviate the user's cognitive burden, we use a question and answer wizard interface and provide recommendations to the answers for the user, the recommendations are generated by analyzing the collected attributes of each document and the user's experiences about them.
