Deep Sets
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Related Articles from SNS
S2M-Trek: From Single to Multi-Sphere Transport via Per-Frame Deep Sets on a Wheel-Legged Robot
new Abstract: We study the problem of scaling dynamic loco-manipulation from a single free-rolling sphere to multiple spheres transported simultaneously on the back of a wheel-legged quadruped, without fences, grippers, or mechanical stops. Multiple identical free-rolling spheres form an unordered set with no persistent identity: their ordering may change independently at each history frame, creating a \emph{per-frame permutation symmetry} that standard history-concatenation set encoders do...
Celestron Origin Intelligent Home Observatory Mark II smart telescope review
Space Verdict Celeston's newest smart telescope serves up incredible images of deep sky objects and the moon, but those looking for a more traditional experience might feel limited. Pros - + Clear deep sky photos in seconds - + Quick set up - + Easy-to-use, helpful app - + Stacks and processes images automatically - + Can be operated from a distance, allowing you to remain inside Cons - - Planetary viewing can be underwhelming - - Large and heavy - - Connecting to devices can be tricky at...
Four key checks before infamous hantavirus cruise ship MV Hondius was allowed to finally return to sea
Four key checks before infamous hantavirus cruise ship MV Hondius was allowed to finally return to sea The MV Hondius is set to sail again and will take new passengers in just a few days' time after completing a deep decontamination programme following a deadly hantavirus outbreak A cruise ship which sparked a global health crisis after a deadly outbreak of hantavirus has been cleared to set sail again after a deep clean. Three people died and another 10 were infected after the rat-borne...
Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics
Announce Type: new Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and difficult to apply in clinical settings. This study developed a deep learning framework to predict these hip dynamics parameters directly from lower-limb gait kinematics and compared three representative sequence models under a unified protocol. Gait data were collected from 60 healthy adults under three...
Sabalenka says she fell into 'deep, dark hole' mentally in Paris shock
Sabalenka says she fell into 'deep, dark hole' mentally in Paris shock PARIS, June 3 : Aryna Sabalenka said she fell into a "deep, dark hole" during her three-sets loss to Russian Diana Shnaider in the French Open quarter-finals on Wednesday, after the world number one let her opportunities slip in the second set. The Belarusian handled the blustery conditions to win the opening set, forged a commanding lead in the second and was two points away from victory, when the contest began to turn...
Expected Return Symmetries
arXiv:2502.01711v3 Announce Type: replace Abstract: Symmetry is an important inductive bias that can improve model robustness and generalization across many deep learning domains. In multi-agent settings, a priori known symmetries have been shown to address a fundamental coordination failure mode known as mutually incompatible symmetry breaking; e.g. in a game where two independent agents can choose to move "left'' or "right'', and where a reward of +1 or -1 is received when the agents...
Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition
arXiv:2404.10370v4 Announce Type: replace Abstract: Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within the realm of deep learning, neural classifiers trained on a closed set of data typically struggle to identify novel classes, leading to erroneous predictions. To address this issue, various heuristic methods have been proposed, allowing models to express uncertainty by stating "I don't know."
End-to-End Subgraph Detection with GraphDETR
Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph. This problem is fundamental across scientific domains and is closely related to subgraph isomorphism, which is NP-complete, limiting combinatorial approaches to small patterns or moderately sized graphs. We introduce GraphDETR, a deep learning framework that formulates subgraph detection as a set prediction problem, analogous to DETR in object detection.
Counterfactual Explanations for Deep Two-Sample Testing
Announce Type: cross Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images. Recent deep two-sample tests improve sensitivity in these settings by learning informative representations, yet they provide limited insight into which data features drive rejection of the null hypothesis $H_0$. To address this issue,...