Credal and Interval Deep Evidential Classifications

Intel has signed a term sheet to acquire chip startup SambaNova, marking a significant step in its expansion within the semiconductor industry. This agreement indicates Intel's strategic interest in enhancing its capabilities in AI and advanced computing technologies.

Intel has confirmed the upcoming release of its "Big Battlemage" GPU, with the Arc B770 expected to debut at CES 2026. This announcement follows the recent appearance of the G31 on Intel's official website, indicating its support for the company's performance analysis tool, VTune Profiler.

The Trump administration has approved Nvidia's sale of H200 artificial intelligence chips to China, allowing the company to proceed with exports contingent upon a 25% profit cut to the U.S. government. This decision follows extensive negotiations and reflects a significant shift in U.S. policy regarding technology exports to China.

Tata and Intel have formed an alliance to develop a silicon and compute ecosystem in India, focusing on the manufacturing and packaging of Intel products at Tata Electronics' upcoming facilities. This collaboration aims to enhance India's semiconductor capabilities and foster local production.

Recent research has identified an 'Inductive Bottleneck' in Vision Transformers (ViTs), where these models exhibit a U-shaped entropy profile, compressing information in middle layers before expanding it for final classification. This phenomenon is linked to the semantic abstraction required by specific tasks and is not merely an architectural flaw but a data-dependent adaptation observed across various datasets such as UC Merced, Tiny ImageNet, and CIFAR-100.

A recent study has introduced a pruned version of Capsule Networks (CapsNets), demonstrating that it can operate up to 9.90 times faster than traditional architectures by eliminating 95% of Primary Capsules while maintaining accuracy across various datasets, including MNIST and CIFAR-10.

A new paper introduces an innovative dataset quantization method aimed at reducing storage and communication costs for large-scale datasets on resource-constrained edge devices. This approach focuses on compressing individual samples by minimizing intra-sample redundancy while retaining essential features, marking a shift from traditional inter-sample redundancy methods.

The CLUster attEntion Network (CLUENet) has been introduced as a novel deep architecture aimed at enhancing visual semantic understanding by addressing the limitations of existing convolutional and attention-based models, particularly their rigid receptive fields and complex architectures. This innovation incorporates global soft aggregation, hard assignment, and improved cluster pooling strategies to enhance local modeling and interpretability.