Local image feature matching techniques help identify fine-grained visual similarities between two images. Although there is a lot of progress in this area, these advancements don’t account for the generalization capability of image-matching models. Many models that focus on specific visual domains with lots of training data, show lower performance on out-of-domain data compared to… →

The rapid growth of AI and complex neural networks drives the need for efficient hardware that suits power and resource constraints. In-memory computing (IMC) is a promising solution for developing various IMC devices and architectures. Designing and deploying these systems requires a comprehensive hardware-software co-design toolchain that optimizes across devices, circuits, and algorithms. The Internet… →

Financial analysis has always been crucial for interpreting market trends, predicting economic outcomes, and providing investment strategies. This field, traditionally rooted in data, has increasingly turned to artificial intelligence (AI) and algorithmic methods to handle the vast and complex data generated daily. AI’s role in finance has grown significantly, automating tasks once performed by human… →

LLMs demonstrate emergent intelligence with increased parameters, computes, and data, hinting at artificial general intelligence. Despite advancements, deployed LLMs still exhibit errors like hallucinations, bias, and factual inaccuracies. Also, the constant evolution of knowledge challenges their pretraining. Addressing errors promptly during deployment is crucial, as retraining or finetuning is often prohibitively costly, posing sustainability issues… →

Proof assistants like Lean ensure high accuracy in mathematical proofs, addressing the growing complexity of modern mathematics that often leads to errors. Formal languages like Lean, Isabelle, and Coq create computer-verifiable proofs but require significant effort and expertise. Automated theorem proving is increasingly important, with new methods focusing on search algorithms to explore potential solutions.… →

Anthropic AI’s Claude family of models represents a great challenging feat for GPT models in AI technology. With the release of the Claude 3 series, Anthropic has expanded its models’ capabilities and performance, catering to various applications from text generation to advanced vision processing. Let’s have an overview of these developments, highlighting the advancements and… →

Anomaly detection in time series data is a crucial task with applications in various domains, from monitoring industrial systems to detecting fraudulent activities. The intricacies of time series anomalies, including early or delayed detections and varying anomaly durations, are not well captured by conventional metrics like Precision and Recall, intended for independent and identically distributed… →

Data structures and algorithms are crucial for efficient problem-solving in computer science, enabling effective data organization and manipulation for fast, reliable software. Learning them enhances your coding skills and prepares you for technical interviews and real-world challenges. This article lists the top data structure and algorithms courses to help you excel in software development and… →

Google Cloud AI Researchers have introduced LANISTR to address the challenges of effectively and efficiently handling unstructured and structured data within a framework.  In machine learning, handling multimodal data—comprising language, images, and structured data—is increasingly crucial. The key challenge is the issue of missing modalities in large-scale, unlabeled, and structured data like tables and time… →