The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...

Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...

Nguyen Xuan Long, a globally recognized expert in statistical inference and machine learning currently based in the United ...

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...

Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...

Amazon Web Services plans to deploy processors designed by Cerebras inside its data centers, the latest vote of confidence in the startup, which specializes in chips that power artificial-intelligence ...

In the months following Elon Musk’s $44 billion acquisition of Twitter in 2022, my experience with the platform (and perhaps yours too) got quickly, dramatically worse. My algorithmic timeline, better ...

Abstract: Sparse diagnosis techniques for antenna arrays provide an efficient approach to fault diagnosis by leveraging the sparse nature of faulty elements. In practical scenarios, an unknown ...

Nvidia is not just a leader in training, but also in AI inference. AMD has carved out a nice niche in inference, and also has a nice agentic AI opportunity with its CPUs. Broadcom is set to benefit ...

As AI workloads shift from centralized training to distributed inference, the network faces new demands around latency requirements, data sovereignty boundaries, model preferences, and power ...

Abstract: The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for ...