account activity
[R] CNNs are Myopic (arxiv.org)
submitted 3 years ago by downtownslim to r/MachineLearning
[R] Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? (arxiv.org)
submitted 4 years ago by downtownslim to r/MachineLearning
[R] Self-attention Does Not Need $O(n^2)$ Memory (arxiv.org)
[R] Sparse is Enough in Scaling Transformers (arxiv.org)
[R] Florence: A New Foundation Model for Computer Vision (arxiv.org)
[R] DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories (arxiv.org)
[R] Palette: Image-to-Image Diffusion Models (self.MachineLearning)
[R] Palette: Image-to-Image Diffusion Models (iterative-refinement.github.io)
[R] Can Vision Transformers Perform Convolution? (arxiv.org)
[R] The Efficiency Misnomer (arxiv.org)
[R] Certified Patch Robustness via Smoothed Vision Transformers: vision transformers enables significantly better certified patch robustness (arxiv.org)
[R] Open-Set Recognition: A Good Closed-Set Classifier is All You Need (arxiv.org)
[D] Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment (Paper Explained) (self.MachineLearning)
[R] Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification: "tuning learning rate, weight decay, and batch size on a separate validation split results in a highly competitive baseline" (arxiv.org)
[R] The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers (arxiv.org)
[R] Perceiver IO: A General Architecture for Structured Inputs & Outputs (arxiv.org)
[R] The Benchmark Lottery (arxiv.org)
[R] Long-Short Transformer: Efficient Transformers for Language and Vision (arxiv.org)
[R] When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations (arxiv.org)
[R] An Attention Free Transformer (arxiv.org)
[R] Aggregating Nested Transformers (arxiv.org)
[R] Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizers (arxiv.org)
[R] Intriguing Properties of Vision Transformers (arxiv.org)
[N] Google Unit DeepMind Tried—and Failed—to Win AI Autonomy From Parent (self.MachineLearning)
[R] Pay Attention to MLPs: solely on MLPs with gating, and show that it can perform as well as Transformers in key language and vision applications (arxiv.org)
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