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1,167 claims from 736 papers are on the record. 43 have been checked so far; the other 1,124 have no check with a result yet.

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Status: Unchecked ×Keyword: transfer learning ×Clear all

8 claims from 7 papers

1. Computer Science › Advanced Neural Network Applications

   Pruning Convolutional Neural Networks for Resource Efficient Inference

   Molchanov, Tyree, Karras, Aila and Kautz · arXiv (Cornell University) · 2016

   The paper proposes a Taylor-expansion criterion for pruning convolutional kernels, interleaved with fine-tuning, and tests it on transfer learning, a gesture classifier and ImageNet.

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   1. UncheckedA new Taylor-expansion pruning criterion is reported to beat weight-norm and activation criteria when pruning large CNNs adapted to Birds-200 and Flowers-102.“The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient informat…”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Modeling aspects of the language of life through transfer-learning protein sequences

   Heinzinger, Elnaggar, Wang et al. · BMC Bioinformatics · 2019

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   1. Unchecked“At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches.”
   2. Unchecked“Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s.”

3. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Evaluating Protein Transfer Learning with TAPE

   Rao, Bhattacharya, Thomas et al. · PubMed · 2019

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   1. Unchecked“We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases.”

4. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

   Li, Amini, Yue, Yang and Lu · bioRxiv (Cold Spring Harbor Laboratory) · 2024

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   1. Unchecked“We observe that while almost all down-stream tasks do benefit from pretrained models compared to naive sequence representations, for the majority of tasks performance does not scale with pretraining, and instead relies on low-level features learned early in…

5. Computer Science › Advanced Neural Network Applications

   The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

   Chen, Frankle, Chang et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“Further analyses reveal that subnetworks found from different pre-training tend to yield diverse mask structures and perturbation sensitivities.”

6. Computer Science › Advanced Neural Network Applications

   Sparse Transfer Learning via Winning Lottery Tickets

   Mehta · arXiv (Cornell University) · 2019

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   1. Unchecked“We show that sparse sub-networks with approximately 90-95% of weights removed achieve (and often exceed) the accuracy of the original dense network in several realistic settings.”

7. Computer Science › Advanced Neural Network Applications

   Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

   Lin, Ji, Li, Deng and Li · arXiv (Cornell University) · 2019

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   1. Unchecked“AULM follows the principle of ADMM and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5x to the most recent work that directly solves the group sparsity-based regula…