Lately, there has been a surge in interest surrounding generative modeling of time series data. Most existing approaches are designed either to process short sequences or to handle long-range sequences. This dichotomy can be attributed to gradient issues with recurrent networks, computational costs associated with transformers, and limited expressiveness of state space models. Towards a unified generative model for varying-length time series, we propose in this work to transform sequences into images.

We introduce CogVLM, a powerful open-source visual language foundation model.

MLaaS are limited by their reliance on training samples or white-box model analysis, highlighting the need for a black-box backdoor purification method.

Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

Deep learning technologies have demonstrated remarkable performance in vulnerability detection.

This highlights the importance of selecting an appropriate schedule for each dataset.

Interaction-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in To deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [2022] study a version of IGL with context-dependent feedback, but their algorithm does not come with theoretical guarantees. Building on this estimator, we propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting.

Gemma-7B fine-tuned with PiSSA achieves an accuracy of 77.7%, surpassing LoRA's 74.53% by 3.25%. Due to the same architecture, PiSSA is also compatible with quantization to further reduce the memory requirement of fine-tuning.

By exploiting students' response logs, SCM aims to predict their exercise

Specifically, it achieves 93.9%, 96.5%, and 94.6% Recall@1 on the MSLS V alidation, Pitts250k-test, and SPED datasets, respectively, while saving 64.3% of trainable parameters compared with the existing SOT A PEFT method.