One of the significant challenges in AI research is the computational inefficiency in processing visual tokens in Vision Transformer (ViT) and Video Vision Transformer (ViViT) models. These models process all tokens with equal emphasis, overlooking the inherent redundancy in visual data, which results in high computational costs. Addressing this challenge is crucial for the deployment… →

Large language models (LLMs) have seen rapid advancements, making significant strides in algorithmic problem-solving tasks. These models are being integrated into algorithms to serve as general-purpose solvers, enhancing their performance and efficiency. This integration combines traditional algorithmic approaches with the advanced capabilities of LLMs, paving the way for innovative solutions to complex problems. The primary… →

Working with Lean, a popular proof assistant for formalizing mathematics, is challenging sometimes. The process of developing proofs in Lean can be time-consuming and complex, especially for those who are new to the system. This complexity can slow down the progress of formalizing mathematical theories. Several tools and methods have been developed to assist with… →

Google DeepMind has unveiled a significant addition to its family of lightweight, state-of-the-art models with the release of Gemma 2 2B. This release follows the previous release of the Gemma 2 series. It includes various new tools to enhance these models’ application and functionality in diverse technological and research environments. The Gemma 2 2B model… →

Time series data, representing observations recorded sequentially over time, permeate various aspects of nature and business, from weather patterns and heartbeats to stock prices and production metrics. Efficiently processing and forecasting these data series can offer significant advantages, such as strategic business planning and anomaly detection in complex systems. However, despite the numerous models and… →

The quick development of Large Language Models (LLMs) has had a big impact on a number of different domains, like generative AI, Natural Language Understanding, and Natural Language Processing. However, hardware limitations have historically made running these models locally on a laptop, desktop, or mobile device difficult. To overcome this issue, the PyTorch team has… →

Direct Preference Optimization (DPO) is an advanced training method to fine-tune large language models (LLMs). Unlike traditional supervised fine-tuning, which depends on a single gold reference, DPO trains models to differentiate between the quality of various candidate outputs. This technique is crucial for aligning LLMs with human preferences, enhancing their ability to generate desired responses… →

Early work established polynomial-time algorithms for finding the densest subgraph, followed by explorations of size-constrained variants and extensions to multiple graph snapshots. Researchers have also investigated overlapping dense subgraphs and alternative density measures. Various algorithmic approaches, including greedy and iterative methods, have been developed to address these challenges. The paper builds on this foundation by… →