Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,932 papers · 148 categories

Trend · papers per month

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0.3%0.5%0.8%0.1% · Nov 202119922001200920172026
6 results for “SELFIES”
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representationcs.LG

SELFIES solves molecular string representation weaknesses for material design.

problem Weaknesses in SMILES for representing valid molecules in material design.
method Introducing SELFIES, a 100% robust string-based molecular representation.
result SELFIES strings correspond to valid molecules, allowing arbitrary machine learning applications.
SELFIESmolecular representationmaterial designvalid moleculesmachine learning
2019-05-31abs ↗pdf ↗
Selfie: Self-supervised Pretraining for Image Embeddingcs.LG

Selfie pretrains image embeddings without pixel-level predictions.

problem Training image embeddings efficiently with limited labeled data.
method Contrastive Predictive Coding loss for masked patches, using convolutional and attention mechanisms.
result Significant improvement in ResNet-50 accuracy on ImageNet 224 x 224 with 60 examples per class (5%).
image embeddingpretrainingcontrastive predictive codingmasked patchesresnet
2019-06-07abs ↗pdf ↗
Cross-Domain Deep Face Matching for Real Banking Security Systemscs.CV

Ensuring the security of transactions is currently one of the major challenges that banking systems deal with. The usage of face for biometric authentication of users is attracting large investments from banks worldwide due to its convenience and acceptability by people, especially in cross-domain scenarios, in which f…

2018-06-20abs ↗pdf ↗
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lensescs.LG

GANs can bias synthetic data, affecting minority and female faces.

problem GANs can amplify biases in synthetic data augmentation.
method Examine GANs on face-shots with gender and skin tone biases.
result GANs generate biased synthetic data, skewing minority modes and features.
Generative Adversarial NetworksBiasFacial Data AugmentationSnapchatMachine Learning
2020-01-26abs ↗pdf ↗