Search Results for author:
Found 21 papers, 13 papers with code
Phenaki: Variable Length Video Generation From Open Domain Textual Description
To the best of our knowledge, this is the first time a paper studies generating videos from time variable prompts.
Ranked #4 on
Video Prediction
on BAIR Robot Pushing
When adversarial examples are excusable
Qualitatively, the remaining adversarial errors are similar to test errors on difficult examples.
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints including latency, compute, energy consumption, etc.
Discovering Multi-Hardware Mobile Models via Architecture Search
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.
Can weight sharing outperform random architecture search? An investigation with TuNAS
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.
Ranked #30 on
Neural Architecture Search
on ImageNet
Neural Predictor for Neural Architecture Search
First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracy based on the architecture.
Scaling Up Neural Architecture Search with Big Single-Stage Models
In this work, we propose BigNAS, an approach that simplifies this workflow and scales up neural architecture search to target a wide range of model sizes simultaneously.
iNNvestigate neural networks!
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
Understanding and Simplifying One-Shot Architecture Search
There is growing interest in automating neural network architecture design.
A Benchmark for Interpretability Methods in Deep Neural Networks
We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks.
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #6 on
Formation Energy
on Materials Project
Formation Energy
Chemical Physics
Materials Science
An Empirical Study on The Properties of Random Bases for Kernel Methods
Kernel machines as well as neural networks possess universal function approximation properties.
The (Un)reliability of saliency methods
Saliency methods aim to explain the predictions of deep neural networks.
Don't Decay the Learning Rate, Increase the Batch Size
We can further reduce the number of parameter updates by increasing the learning rate $\epsilon$ and scaling the batch size $B \propto \epsilon$.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Ranked #1 on
Time Series
on QM9
Learning how to explain neural networks: PatternNet and PatternAttribution
We show that these methods do not produce the theoretically correct explanation for a linear model.
Investigating the influence of noise and distractors on the interpretation of neural networks
Understanding neural networks is becoming increasingly important.
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling
The usability of Brain Computer Interfaces (BCI) based on the P300 speller is severely hindered by the need for long training times and many repetitions of the same stimulus.
