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.
Backpropagation applied to handwritten zip code recognition
9 Pith papers cite this work. Polarity classification is still indexing.
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A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
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.
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.
ARROW adds a distribution-matching long-term replay buffer to DreamerV3 and shows reduced forgetting versus same-size baselines on Atari and Procgen continual RL benchmarks.
A geodesic operator pre-processing step is introduced to let FCNs exploit topological information for segmenting histological images of pigmented reconstructed epidermis.
libconform v0.1.0 is a Python library that implements core conformal prediction algorithms and exposes a documented API.
An overview of deep learning applications and challenges in the automotive industry, covering ADAS, automated driving, virtual sensing, and data-driven development.
citing papers explorer
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On What We Can Learn from Low-Resolution Data
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.
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Zero-shot World Models Are Developmentally Efficient Learners
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
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Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
A CNN detects 19,685 LAEs at z=2-3.5 in DESI DR1 spectra with 95% purity and completeness.
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Strait: Perceiving Priority and Interference in ML Inference Serving
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.
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ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
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.
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ARROW: Augmented Replay for RObust World models
ARROW adds a distribution-matching long-term replay buffer to DreamerV3 and shows reduced forgetting versus same-size baselines on Atari and Procgen continual RL benchmarks.
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Dealing with Topological Information within a Fully Convolutional Neural Network
A geodesic operator pre-processing step is introduced to let FCNs exploit topological information for segmenting histological images of pigmented reconstructed epidermis.
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libconform v0.1.0: a Python library for conformal prediction
libconform v0.1.0 is a Python library that implements core conformal prediction algorithms and exposes a documented API.
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Deep Learning in the Automotive Industry: Recent Advances and Application Examples
An overview of deep learning applications and challenges in the automotive industry, covering ADAS, automated driving, virtual sensing, and data-driven development.