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Found 15 papers, 4 papers with code
Adaptive Transfer Learning: a simple but effective transfer learning
Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data.
The Outer Product Structure of Neural Network Derivatives
In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not.
Doing the impossible: Why neural networks can be trained at all
We show that adding structure to the neural network that enforces higher mutual information between layers speeds training and leads to more accurate results.
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
Understanding and Exploiting the Low-Rank Structure of Deep Networks
Training methods for deep networks are primarily variants on stochastic gradient descent.
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks.
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software.
How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks.
Dynamic Input Structure and Network Assembly for Few-Shot Learning
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception.
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40, 000 compounds.
Deep Learning for Computational Chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry.
Mutual information for fitting deep nonlinear models
In the present work we investigate the use of information theoretic measures such as mutual information and Kullback-Leibler (KL) divergence as objective functions for fitting such models without knowledge of the hidden layer.
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
In this paper we present a technique to train neural network models on small amounts of data.
