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communities.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18889","ref_index":15,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Soft Learning","primary_cat":"cs.LG","submitted_at":"2026-05-16T22:14:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17131","ref_index":81,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation","primary_cat":"cs.CV","submitted_at":"2026-05-16T19:37:41+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Nature323, 6088 (01 Oct 1986), 533-536. doi:10.1038/323533a0 [80] Radu Bogdan Rusu and Steve Cousins. 2011. 3D is here: Point Cloud Library (PCL). InIEEE International Conference on Robotics and Automation (ICRA). IEEE, Shanghai, China. Manuscript submitted to ACM A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation 27 [81] Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli, George Bebis, and Javad Sattarvand. 2024. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation.Machine Vision and Applications35, 4 (2024). [82] Jonathan Sauder and Bjarne Sievers. 2019. Self-Supervised Deep Learning on Point Clouds by Reconstructing Space."},{"citing_arxiv_id":"2605.10958","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov-Arnold Networks","primary_cat":"physics.ao-ph","submitted_at":"2026-05-04T22:43:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"pKANrtm uses a physics-aware multi-fidelity KAN to emulate high-fidelity radiative transfer coefficients for atmospheric correction with superior accuracy and large speedups over direct libRadtran runs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"for scientific regression and RTM emulation [42,43]. KANs provide a newer alternative inspired by the Kolmogorov-Arnold representation theorem, using learnable univariate functions on edges rather than fixed nodal activations [ 36]. Practical implementations such as efficient-kan make KAN-family models easier to evaluate in applied scientific regression settings [ 44]. However, KANs have not yet been widely studied for atmo- spheric RTM emulation. To the best of our knowledge, SRF-aware multi-fidelity emulation of atmospheric-correction coefficients from paired 6S and libRadtran simulations using physics-guided KAN models remains unexplored. This gap motivates the proposed pKAN- rtm framework. 3. Materials and Methods"},{"citing_arxiv_id":"2605.02300","ref_index":234,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Meta Reinforcement Learning Approach to Goals-Based Wealth Management","primary_cat":"cs.LG","submitted_at":"2026-05-04T07:48:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.02950","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces","primary_cat":"cs.LG","submitted_at":"2026-05-01T17:46:26+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"are evaluated on the same query set and the resulting per-que ry effectiveness values are neither independent across systems nor naturally justified by Gaussian assumptions. In information-retrieval evaluation, paired bootstrap proc e- dures are a standard way to quantify uncertainty and compare systems while preserving the dependence induced by shared test queries [37]. Among the reported measures, the rank-sensitive retrieval criteria are the primary basis for the paper's architectura l claim. This is because the deployment objective studied her e is not merely approximate teacher-space reconstruction, but accurate law identification near the top of the ranking un der a lightweight serving-time path. The consensus-"},{"citing_arxiv_id":"2605.00070","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"CRADIPOR: Crash Dispersion Predictor","primary_cat":"cs.LG","submitted_at":"2026-04-30T11:10:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A rank reduction autoencoder combined with classification predicts numerical dispersion in automotive crash simulations more effectively than random forests when using wavelet or slope signal inputs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26398","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Phenomenological Classification of TESS Eclipsing Binaries","primary_cat":"astro-ph.SR","submitted_at":"2026-04-29T08:08:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A neural network classifies 20,196 TESS eclipsing binaries into 13,376 EA, 2,114 EB, and 4,706 EW systems after achieving 99% accuracy on held-out test data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26219","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem","primary_cat":"cs.CR","submitted_at":"2026-04-29T01:53:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"eDySec is a deep learning-based framework that detects malicious PyPI packages through dynamic analysis, halving feature dimensionality, reducing false positives by 82%, false negatives by 79%, and boosting accuracy by 3% with near-perfect stability.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Williams. 1986. Learning Representations by Back-Propagating Errors.Nature323, 6088 (1986), 533-536. doi:10.1038/323533a0 [49] Khuloud Saeed Alketbi and Abid Mehmood. 2025. A Comprehensive Survey of Explainable Artificial Intelligence Techniques for Malicious Insider Threat Detection.IEEE Access13 (2025), 121772-121798. doi:10.1109/ACCESS.2025. 3587114 [50] Safety Research Team. 2026.Fake Grok API Wrapper Deploys New Malware. https://www.getsafety.com/blog-posts/grokwrapper Accessed: Apr. 9, 2026. [51] Haya Samaana, Diego Elias Costa, Emad Shihab, and Ahmad Abdellatif. 2025. A Machine Learning-Based Approach for Detecting Malicious PyPI Packages. InProceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC"},{"citing_arxiv_id":"2604.18781","ref_index":162,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans","primary_cat":"cs.CV","submitted_at":"2026-04-20T19:45:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18319","ref_index":185,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference","primary_cat":"stat.ML","submitted_at":"2026-04-20T14:20:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11507","ref_index":108,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers","primary_cat":"math.OC","submitted_at":"2026-04-13T14:11:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[106] Rockafellar RT, Wets RJB (1998)Variational Analysis(Berlin, Heidelberg: Springer), URLhttp://dx.doi. org/10.1007/978-3-642-02431-3. [107] Romera-ParedesB,BarekatainM,NovikovA,BalogM,KumarMP,DupontE,RuizFJR,EllenbergJS,WangP, Fawzi O, Kohli P, Fawzi A (2024) Mathematical discoveries from program search with large language models. Nature625:468-475, URLhttp://dx.doi.org/10.1038/s41586-023-06924-6. [108] Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors.Nature 323(6088):533-536, URLhttp://dx.doi.org/10.1038/323533a0. [109] Sadana U, Chenreddy A, Delage E, Forel A, Frejinger E, Vidal T (2025) A survey of contextual optimization methods for decision-making under uncertainty.European Journal of Operational Research320(2):271-289,"},{"citing_arxiv_id":"2512.23962","ref_index":147,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Lectures on insulating and conducting quantum spin liquids","primary_cat":"cond-mat.str-el","submitted_at":"2025-12-30T03:26:34+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"While much insight can be gained from the methods above, they fall short of providing a mean- field theory for the FL* phase, which could be used to study quantum phase transitions out of it. A suitable mean field theory can also lead to a trial wavefunction for the FL* state, which can be used for variational numerical computations and compared to cold atom observations [144-147], as discussed in Section 7.4. To these ends, we now describe the Ancilla Layer Model (ALM)[148, 149], which is designed to automatically ensure consistency with anomaly-type arguments. We wish to have a mean-field theory which changes a large hole-like Fermi surface of area (1+p)/2 to small hole-like Fermi surfaces of total areap/2. The simplest way to achieve this"},{"citing_arxiv_id":"2508.02750","ref_index":66,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Pulse Shape Discrimination Algorithms: Survey and Benchmark","primary_cat":"cs.LG","submitted_at":"2025-08-03T04:41:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A survey and benchmark of ~60 PSD algorithms on two radiation datasets finds deep learning models (MLPs and hybrids) often outperform traditional statistical methods, with an open-source Python/MATLAB toolbox and datasets released.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2412.01459","ref_index":65,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI","primary_cat":"cs.CY","submitted_at":"2024-12-02T12:51:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}