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Found 18 papers, 6 papers with code
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
With regards to data, we demonstrate the significance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality.
AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions
Computers calculate transcendental functions by approximating them through the composition of a few limited-precision instructions.
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM).
Scalable Diffusion for Materials Generation
Lastly, we show that conditional generation with UniMat can scale to previously established crystal datasets with up to millions of crystals structures, outperforming random structure search (the current leading method for structure discovery) in discovering new stable materials.
Predicting emergence of crystals from amorphous matter with deep learning
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
For example, even the simple flips-and-crops augmentation requires training for more than 5 epochs to improve performance, whereas RandAugment requires more than 90 epochs.
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries.
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Data augmentations are important in training high-performance 3D object detectors for point clouds.
What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries
Deep neural network classifiers partition input space into high confidence regions for each class.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
Ranked #107 on
Image Classification
on ImageNet
Learn2Hop: Learned Optimization on Rough Landscapes
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material design.
Tradeoffs in Data Augmentation: An Empirical Study
Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood.
Deconstructing the Regularization of BatchNorm
Surprisingly, this simple mechanism matches the improvement of $0. 8\%$ of the more complex Dropout regularization for the state-of-the-art Efficientnet-B8 model on Imagenet.
Slice, Dice, and Optimize: Measuring the Dimension of Neural Network Class Manifolds
Deep neural network classifiers naturally partition input space into regions belonging to different classes.
JAX MD: A Framework for Differentiable Physics
We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics.
Does Data Augmentation Benefit from Split BatchNorms
Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision.
Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D point clouds.
