Deep Learning
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Even in large language models, negative transfer problems may still exist.
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Even in large language models, negative transfer problems may still exist.
DATA: Differentiable ArchiTecture Approximation
Jianlong Chang, xinbang zhang, Yiwen Guo, GAOFENG MENG, SHIMING XIANG, Chunhong Pan
Neural Information Processing Systems http://nips.cc/
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD Xiaoyi Dong
The Large Vision-Language Model (L VLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution.
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming
Fei Wang, James Decker, Xilun Wu, Gregory Essertel, Tiark Rompf
In this paper we propose an implementation of backpropagation using functions with callbacks, where the forward pass is executed as a sequence of function calls, and the backward pass as a corresponding sequence of function returns. A key realization is that this technique of chaining callbacks is well known in the programming languages community as continuation-passing style (CPS) .
Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation
However, empirically, we find that, directly using the native transformer to model IMU sequences results in unacceptable jitter and inaccurate postures.
Subject-driven Text-to-Image Generation via Apprenticeship Learning Wenhu Chen Hexiang Hu Y andong Li Nataniel Ruiz Xuhui Jia Ming-Wei Chang William W. Cohen Google Deepmind
Subject-driven image generation is related to text-driven image editing but often needs to perform more sophisticated transformations to source images (e.g., rotating the view, zooming in/out, changing the pose of
