Transformers as support vector machines
Since its inception in" Attention Is All You Need", transformer architecture has led to
revolutionary advancements in NLP. The attention layer within the transformer admits a …
revolutionary advancements in NLP. The attention layer within the transformer admits a …
Primal-attention: Self-attention through asymmetric kernel svd in primal representation
Recently, a new line of works has emerged to understand and improve self-attention in
Transformers by treating it as a kernel machine. However, existing works apply the methods …
Transformers by treating it as a kernel machine. However, existing works apply the methods …
Insect-foundation: A foundation model and large-scale 1m dataset for visual insect understanding
In precision agriculture the detection and recognition of insects play an essential role in the
ability of crops to grow healthy and produce a high-quality yield. The current machine vision …
ability of crops to grow healthy and produce a high-quality yield. The current machine vision …
SURE: SUrvey REcipes for building reliable and robust deep networks
In this paper we revisit techniques for uncertainty estimation within deep neural networks
and consolidate a suite of techniques to enhance their reliability. Our investigation reveals …
and consolidate a suite of techniques to enhance their reliability. Our investigation reveals …
Decoding class dynamics in learning with noisy labels
The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …
labels, leading to reduced generalization in deep-learning models. Sample selection-based …
MS-DINO: Masked self-supervised distributed learning using vision transformer
Despite promising advancements in deep learning in medical domains, challenges still
remain owing to data scarcity, compounded by privacy concerns and data ownership …
remain owing to data scarcity, compounded by privacy concerns and data ownership …
[HTML][HTML] Computational techniques for virtual reconstruction of fragmented archaeological textiles
D Gigilashvili, H Lukesova… - Heritage …, 2023 - heritagesciencejournal …
Archaeological artifacts play important role in understanding the past developments of the
humanity. However, the artifacts are often highly fragmented and degraded, with many …
humanity. However, the artifacts are often highly fragmented and degraded, with many …
Learning with noisy labels via Mamba and entropy KNN framework
Learning from corrupted data marginally degrades model performance. As deep learning
proliferates, the need for large, accurately labeled datasets becomes crucial. Central to this …
proliferates, the need for large, accurately labeled datasets becomes crucial. Central to this …
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
S Khallaghi, JR Eastman, LD Estes - arXiv preprint arXiv:2308.09221, 2023 - arxiv.org
Semantic segmentation (classification) of Earth Observation imagery is a crucial task in
remote sensing. This paper presents a comprehensive review of technical factors to …
remote sensing. This paper presents a comprehensive review of technical factors to …
GANzzle++: Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations
Jigsaw puzzles are a popular and enjoyable pastime that humans can easily solve, even
with many pieces. However, solving a jigsaw is a combinatorial problem, and the space of …
with many pieces. However, solving a jigsaw is a combinatorial problem, and the space of …