{"total":14,"items":[{"citing_arxiv_id":"2606.21297","ref_index":53,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"NASDAQ: Normalized Observation Space Dynamics-Augmented Q-Learning","primary_cat":"cs.LG","submitted_at":"2026-06-19T10:21:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NASDAQ normalizes observations in an online RL setting so that dynamics prediction losses are balanced across dimensions, yielding competitive performance with lower wall-time than prior model-based and self-predictive methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10969","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Functional Data Framework For Analyzing Shapes and Textures in Images","primary_cat":"stat.ME","submitted_at":"2026-06-09T15:11:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Proposes a frugal functional representation for star-shaped image objects to analyze contours and textures, illustrated on supervised classification.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01910","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Single-Line Drawing Generation via Semantics-Driven Optimization","primary_cat":"cs.GR","submitted_at":"2026-06-01T08:46:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A semantics-driven optimization of URBS curves via score distillation sampling produces single continuous line drawings from text prompts or images.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00950","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space","primary_cat":"cs.LG","submitted_at":"2026-05-31T02:04:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"COLLIE constructs a semantically coherent skill latent space from unsupervised data to enable training-free guidance with sparse online feedback in guided skill discovery.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.29428","ref_index":46,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"DELOS: Detecting Shallow Transits in Kepler Photometry Using a Contrastive-Learning Framework","primary_cat":"astro-ph.EP","submitted_at":"2026-05-28T06:22:22+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"DELOS applies contrastive learning to phase-folded light curves to detect shallow intermediate-to-long period transits, reporting 15.5% and 11.25% gains in combined precision-recall over BLS and TLS in low-SNR tests plus 3-80x speedups.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12503","ref_index":41,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data","primary_cat":"astro-ph.GA","submitted_at":"2026-05-12T17:59:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12168","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On What We Can Learn from Low-Resolution Data","primary_cat":"cs.LG","submitted_at":"2026-05-12T14:16:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"= log   1 Zl exp \u0010 −PN i=1 ℓ(θ,x i)−ℓ(θ,x l) \u0011 1 Zh exp \u0010 −PN i=1 ℓ(θ,x i)−ℓ(θ,x h) \u0011   (40) = log \u0012 Zh Zl \u0013 + (ℓ(θ,x h)−ℓ(θ,x l)).(41) Introducing∆(θ) =ℓ(θ,x h)−ℓ(θ,x l)for notational convenience. A rewrite ofZ h yields, Zh = Z exp − NX i=1 ℓ(θ,x i)−ℓ(θ,x l) ! exp (ℓ(θ,x l)−ℓ(θ,x h))dθ(42) = Z Zlp(θ| X l) exp (−∆(θ))dθ(43) =Z lEθ|Xl[exp (−∆(θ))].(44) Substitute back into eq. (41): log \u0012 Zh Zl \u0013 + ∆(θ) = log Eθ|Xl[exp(−∆(θ))] \u0001 + ∆(θ),(45) and subsequently into eq. (39), the difference in KL divergence admits the following closed-form expression: KLh −KL l = Z p(θ| X) log Eθ|Xl[exp(−∆(θ))] \u0001 + ∆(θ) \u0001 dθ(46) = log Eθ|Xl[exp(−∆(θ))] \u0001 +E θ|X [∆(θ)].(47) The use of the cumulant generating function used in Proposition 4 can similarly be applied here:"},{"citing_arxiv_id":"2604.28175","ref_index":62,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Strait: Perceiving Priority and Interference in ML Inference Serving","primary_cat":"cs.LG","submitted_at":"2026-04-30T17:55:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Strait cuts high-priority deadline violations in ML inference serving by 1-11 percentage points through contention modeling and priority scheduling under high GPU load.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"L2 and DRAM throughputs; compute activity is ignored as gpuletsemploys Multi-Process Service (MPS) [ 5] to partition compute resources. Therefore, we additionally include the throughput of Streaming Multiprocessors (SMs), the basic compute units, to approximate the overall compute activity. We first deploymodel set 1(ResNet-50 [ 44], ViT-B-16 [35], ConvNeXt-B [62]) and collect serving data, using 80% of it for training and 20% for validation. Figure 2 presents the cu- mulative distribution function (CDF) of the absolute relative iii Haidong Zhao and Nikolaos Georgantas Model 1 instances GPU Instance poolModel 1: high-priority Running Task ListM2-B3 Async. ControlSubmit Inference DeadlineInterference PredictionPriority-awareBatch Size: 3 Remove completed tasks"},{"citing_arxiv_id":"2604.10333","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Zero-shot World Models Are Developmentally Efficient Learners","primary_cat":"cs.AI","submitted_at":"2026-04-11T19:32:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"achieve data-efficient and flexible (zero-shot) visual cognition from early experience? We approach this question using computational models of visual learning and development. Inspired by neurophysiological observations [7, 8], deep neural networks (DNNs) have emerged as the most task-performant models for visual tasks as well as the most accurate models of neural responses across the visual cortex [9, 10, 11, 12, 13] and for human-like error patterns [14]. Initially, DNNs required supervision on large labeled datasets [15, 16] and did not transfer broadly to downstream tasks. These issues motivated a shift to self-supervised models, which learn representations by grouping similar or temporally-proximate images [ 17, 18, 19, 20, 21, 22, 23, 24]."},{"citing_arxiv_id":"2604.07437","ref_index":58,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier","primary_cat":"astro-ph.IM","submitted_at":"2026-04-08T18:00:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.11395","ref_index":17,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ARROW: Augmented Replay for RObust World models","primary_cat":"cs.LG","submitted_at":"2026-03-12T00:15:11+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.02015","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"libconform v0.1.0: a Python library for conformal prediction","primary_cat":"cs.LG","submitted_at":"2019-07-03T16:24:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"libconform v0.1.0 is a Python library that implements core conformal prediction algorithms and exposes a documented API.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1906.11600","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Dealing with Topological Information within a Fully Convolutional Neural Network","primary_cat":"cs.CV","submitted_at":"2019-06-27T13:05:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A geodesic operator pre-processing step is introduced to let FCNs exploit topological information for segmenting histological images of pigmented reconstructed epidermis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1906.08834","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Deep Learning in the Automotive Industry: Recent Advances and Application Examples","primary_cat":"cs.LG","submitted_at":"2019-06-20T20:30:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":0.0,"formal_verification":"none","one_line_summary":"An overview of deep learning applications and challenges in the automotive industry, covering ADAS, automated driving, virtual sensing, and data-driven development.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}