Deep Learning
IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs Xi Gao
One widely used approach for addressing NLPs is the interior point method (IPM).
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction Qilong Ma
Figure 1: Comparison between Lagrangian (left) and Eulerian (right) perspectives.
Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.
Zhaoxuan Wu*, Xiaoqiang Lin
On the other hand, the impact of the instruction, another essential component in the prompt given to the LLM, is often overlooked in existing exemplar selection methods.
Diffuser Lite: Towards Real-time Diffusion Planning Zibin Dong 1 Jianye Hao
The capability of generating high-quality long-horizon trajectories makes it a promising research direction.
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.
A Sober Look at the Robustness of CLIPs to Spurious Features Qizhou Wang 1 Yong Lin 2 Y ongqiang Chen 3 Ludwig Schmidt 4
We raise a challenging research question in the following: Is there a benchmark that reflects the exact reliance on spurious features of CLIP?
