github.com/zhoukaisheng/Landslide-Susceptibility
zhoukaisheng/Landslide-Susceptibility
Landslide susceptibility mapping by deep learning and slope unit (⭐ 77)
arxiv.org/abs/1810.01937v1
LIT: Block-wise Intermediate Representation Training for Model Compression
Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by re...
github.com/TrickyGo/Dive-into-DL-TensorFlow2.0
TrickyGo/Dive-into-DL-TensorFlow2.0
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可 (⭐ 3834)
github.com/ShusenTang/Dive-into-DL-PyTorch
ShusenTang/Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。 (⭐ 19332)
arxiv.org/abs/1910.06392v1
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amin…
Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage....
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Operations - Newell Brands
Feb 18, 2026 · Employee safety is a top priority at Newell Brands. Over the past year, we maintained the programs that have allowed us to achieve a world-class safety record while deepening our training …
github.com/eugeneyan/ml-surveys
eugeneyan/ml-surveys
? Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. (⭐ 2881)
arxiv.org/abs/2211.11432v3
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MAT...
github.com/kuleshov/teaching-material
kuleshov/teaching-material
Teaching materials for the machine learning and deep learning classes at Stanford and Cornell (⭐ 1135)
www.bing.com/ck/a?!&&p=aaa0ff1676fce9d82943ef5dee1d08fa2124445a3cc2c6403ec0b128c559f878JmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=3d77c516-c891-6468-0933-d207c9666525&u=a1aHR0cHM6Ly93d3cuZnJlZXNlb2F1ZGl0c2VydmljZXMuY29tL3Nlby1uZXdzLXJldmlld3MtYXJ0aWNsZXMvYmluZy10ZXN0cy1uZXctbGFiZWxzLWZvci1yZWxhdGVkLXNlYXJjaGVzLWZlYXR1cmUv&ntb=1
Bing Tests New Labels for Related Searches Feature
May 30, 2025 · TL;DR Summary: Action-Oriented Language: Bing is testing new headers like "Get detailed results" and "Take a deep dive" to make the related searches feature more engaging and …
arxiv.org/abs/2602.12380v1
TFT-ACB-XML: Decision-Level Integration of Customized Temporal Fusion Transforme…
Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and generalization a...
arxiv.org/abs/2005.12206v1
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-rankin…
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either directly rank items by an end to end model, or rank items by a score fu...
arxiv.org/abs/2210.06592v2
Can Calibration Improve Sample Prioritization?
Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training? In this paper, we show that it can when used to prioritize some examples for performing subset selection. We study the effect of p...
github.com/PeiranLi0930/Comprehensive_DL_Tutor
PeiranLi0930/Comprehensive_DL_Tutor
Comprehensive Deep Learning Tutorial : From Zero To Hero (⭐ 545)
arxiv.org/abs/2308.03243v1
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Cha…
Adversarial robustness poses a critical challenge in the deployment of deep learning models for real-world applications. Traditional approaches to adversarial training and supervised detection rely on prior knowledge of attack types and access to lab...
github.com/SemiAnalysisAI/InferenceX
SemiAnalysisAI/InferenceX
Open Source Continuous Inference Benchmarking Qwen3.5, DeepSeek, GPTOSS - GB200 NVL72 vs MI355X vs B200 vs GB300 NVL72 vs H100 & soon™ TPUv6e/v7/Trainium2/3 (⭐ 603)
www.bing.com/ck/a?!&&p=b443d54e805e33b3eabe2ea097f62d32889caf94934d707d15bda508a4fff757JmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=1878a654-c797-6d51-1a34-b145c6fd6cd9&u=a1aHR0cHM6Ly90b3JvbnRvLmNpdHluZXdzLmNhL2NhdGVnb3J5L2xvY2FsLw&ntb=1
Local News - CityNews Toronto
Toronto wakes to bitter cold as March begins, but major warmup is on the way Toronto is starting the first week of March in a deep freeze, with wind chills near –20 making for a harsh Monday...
152334H.github.io/blog/scaling-exponents/
Calculating the cost of a Google DeepMind paper
Points: 303 | Comments: 150 | Author: 152334H
arxiv.org/abs/2004.10250v1
Certifying Joint Adversarial Robustness for Model Ensembles
Deep Neural Networks (DNNs) are often vulnerable to adversarial examples.Several proposed defenses deploy an ensemble of models with the hope that, although the individual models may be vulnerable, an adversary will not be able to find an adversarial...
arxiv.org/abs/2110.13223v1
Identifying and Benchmarking Natural Out-of-Context Prediction Problems
Deep learning systems frequently fail at out-of-context (OOC) prediction, the problem of making reliable predictions on uncommon or unusual inputs or subgroups of the training distribution. To this end, a number of benchmarks for measuring OOC perfor...