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1/ New paper on an old topic: turns out, FGSM works as well as PGD for adversarial training!* *Just avoid catastrophic overfitting, as seen in picture Paper: https://arxiv.org/abs/2001.03994 Code: https://github.com/locuslab/fast_adversarial … Joint work with
@_leslierice and@zicokolter to be at#ICLR2020pic.twitter.com/2EmwFaX7Qp
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Our new adversarial attack published @ NeurIPS 2019 is now available for Foolbox and CleverHans! The attack is SOTA in L0, L1, L2 & Linf, needs close to no hyperparameter tuning & is less susceptible to some types of gradient masking. Blog post @ https://medium.com/@wielandbr/accurate-reliable-and-fast-robustness-evaluation-4e2a5ab43521 …pic.twitter.com/JtSfGqjK58
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We distill key components for pre-training representations at scale: BigTransfer ("BiT") achieves SOTA on many benchmarks with ResNet, e.g. 87.8% top-1 on ImageNet (86.4% with only 25 images/class) and 99.3% on CIFAR-10 (97.6% with only 10 images/class). http://arxiv.org/abs/1912.11370 pic.twitter.com/bQULupLQzi
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A General and Adaptive Robust Loss Function https://arxiv.org/abs/1701.03077 They propose an analytical function that can represent a family of well known robust cost functions just with a single parameter (alpha). Alpha lets you walk through L2, huber, cauchy, tukey and more.pic.twitter.com/Uy5nB7DoOR
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Robust regression is obtained by using a l1 loss in place of least squares. Removes outliers. For d=0, computes the median instead of the mean. https://en.wikipedia.org/wiki/Robust_regression …pic.twitter.com/A77Rt3imkZ
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Some folks still seem confused about what deep learning is. Here is a definition: DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization.... https://www.facebook.com/722677142/posts/10156463919392143/ …
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Pretrained checkpoints in Pytorch: https://github.com/rwightman/gen-efficientnet-pytorch … h/t to
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This video explains AdvProp from
@GoogleAI! This technique leverages Adversarial Examples for ImageNet classification by using separate Batch Normalization layers for clean and adversarial mini-batches. https://youtu.be/KTCztkNJm50#100DaysOfMLCodeHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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AR-Net combines the best of both traditional statistical models and neural network models for time series modeling using a feedforward neural network approach. Our proposed model is easier to use and scales well for large volumes of training data. https://ai.facebook.com/blog/ar-net-a-simple-autoregressive-neural-network-for-time-series/ …pic.twitter.com/C07f1oE7P1
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Self-supervised AMDIM learns to represent observations of a shared cause — sights, scents & sounds of baking — driven by a desire to predict related observations — the taste of cookies. MSR Montreal trained SOTA image representations with only 4 GPUs https://aka.ms/AMDIM
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What are the definitive early references for anomaly detection in machine learning?
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A tale of two AI summers: 1980s Now Expert systems Deep learning More rules! More data! LISP machines GPUs Cyc DeepMind Brittleness Brittleness
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Neural Arithmetic Logic Units Paper: https://arxiv.org/pdf/1808.00508.pdf … Code:https://github.com/kgrm/NALU/blob/master/nalu.py …
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Welcome back, gradients! This method is orders of magnitude faster than state-of-the-art non-differentiable techniques. DARTS: Differentiable Architecture Search by Hanxiao Liu, Karen Simonyan, and Yiming Yang. Paper: https://arxiv.org/abs/1806.09055 Code: https://github.com/quark0/darts pic.twitter.com/pIHg3krnAE
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Speech and Language Processing 3rd ed. partial draft of 21 chapters at http://web.stanford.edu/~jurafsky/slp3/ Thanks to all you readers for advice/typos!
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I tend to see few real-world application of Deep RL outside of games. Surprised that I found 3 in today’s papers! Deep RL for input fuzzing (https://arxiv.org/abs/1801.04589 ), characterizing cell movement (https://arxiv.org/abs/1801.04600 ) and wireless communication (https://arxiv.org/abs/1801.04541 ).
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CMU Neural Nets for NLP (13): "Parsing w/ dynamic programs" https://youtu.be/gRtEW6Q5XJE Code example is
@stanfordnlp's Deep Bi-affine Attention.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Ubuntu 17.10 releases with GNOME, Kubernetes 1.8 & minimal base images http://bit.ly/2xSbUWo pic.twitter.com/NMQs6SAcwx
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Beautiful exposition of variational inference (such as
#LDA in gensim) and beyond
A must for all #physics +#ML lovers!
https://twitter.com/thejaan/status/899322238033199104 …
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http://Pydial.org - a new python toolkit from Cambridge University for statistical dialogue systems.
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