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Found 13 papers, 3 papers with code
Imitation of Life: A Search Engine for Biologically Inspired Design
We release data and code; we view BARcode as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.
A Probabilistic Approach to Neural Network Pruning
Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network.
Complex Factoid Question Answering with a Free-Text Knowledge Graph
DELFT's advantage comes from both the high coverage of its free-text knowledge graph-more than double that of dbpedia relations-and the novel graph neural network which reasons on the rich but noisy free-text evidence.
Thermally Regenerative Flow Batteries with pH Neutral Electrolytes for Harvesting Low-Grade Heat
This work also presents a comprehensive model with a coupled analysis of mass transfer and reaction kinetics in a porous electrode that can accurately capture the flow rate dependence of power density and energy conversion efficiency.
Applied Physics Classical Physics
Personalized Visualization Recommendation
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback.
The Impact of the Mini-batch Size on the Dynamics of SGD: Variance and Beyond
We study mini-batch stochastic gradient descent (SGD) dynamics under linear regression and deep linear networks by focusing on the variance of the gradients only given the initial weights and mini-batch size, which is the first study of this nature.
ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5. 92 from a 7-point Likert scale compared to only 3. 45).
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent
The mini-batch stochastic gradient descent (SGD) algorithm is widely used in training machine learning models, in particular deep learning models.
Clustering Degree-Corrected Stochastic Block Model with Outliers
For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from the expected number of edges to other inliers.
Dynamic Cell Structure via Recursive-Recurrent Neural Networks
In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps.
Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square
We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely "combined Neyman-Pearson chi-square" ($\chi^2_{\mathrm{CNP}}$).
Data Analysis, Statistics and Probability High Energy Physics - Experiment Nuclear Experiment
Multimodal Machine Translation with Reinforcement Learning
We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18).
A 20-Liter Test Stand with Gas Purification for Liquid Argon Research
We describe the design of a 20-liter test stand constructed to study fundamental properties of liquid argon (LAr).
Instrumentation and Detectors High Energy Physics - Experiment
