Traditional protein design, often relying on physics-based methods like Rosetta, faces challenges in creating functional proteins with complex structures due…
Machine Learning
Few-shot Generative Domain Adaptation (GDA) is a machine learning and domain adaptation concept that addresses the challenge of adapting a…
Product insights & monitoring, testing, end-to-end analytics, and errors are four of the most difficult LLMs to monitor and test.…
Managing, analyzing, and extracting data from large volumes of documents is a crucial yet challenging task. Traditionally, this has required…
The world’s cultural heritage faces mounting peril from escalating conflicts and natural disasters, jeopardizing ancient sites and artifacts worldwide. Wars,…
As generative artificial intelligence (AI) inference becomes increasingly critical for businesses, customers are seeking ways to scale their generative AI…
In a recent study by Innodata, various large language models (LLMs) such as Llama2, Mistral, Gemma, and GPT were benchmarked…
Complex Human Activity Recognition (CHAR) in ubiquitous computing, particularly in smart environments, presents significant challenges due to the labor-intensive and…
Large Language Models (LLMs) with parametric memory of rules and knowledge have shown limitations in implicit reasoning. Research has shown…
Accurately modeling magnetic hysteresis is a significant challenge in the field of AI, especially for optimizing the performance of magnetic…
Graph comprehension and complex reasoning in artificial intelligence involve developing and evaluating the abilities of Large Language Models (LLMs) to…
The research on vision-language models (VLMs) has gained significant momentum, driven by their potential to revolutionize various applications, including visual…
Having high-quality photographs of products is crucial in the ever-changing realm of online marketing and e-commerce. The use of artificial…
Microsoft has unveiled an extensive AI learning journey designed to deal with the diverse needs of various personas within a…
Adversarial attacks are attempts to trick a machine learning model into making a wrong prediction. They work by creating slightly…
Controllable Learning (CL) is emerging as a crucial component of trustworthy machine learning. It emphasizes ensuring that learning models meet…
We study the problem of private vector mean estimation in the shuffle model of privacy where nnn users each have…
Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora.…
We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere…
Multilingual natural language processing (NLP) is a rapidly advancing field that aims to develop language models capable of understanding &…