Search Results for author:
Found 10 papers, 1 papers with code
A survey and taxonomy of loss functions in machine learning
This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.
Maximum entropy exploration in contextual bandits with neural networks and energy based models
This provides practitioners with new techniques that perform well in static and dynamic settings, and are particularly well suited to non-linear scenarios with continuous action spaces.
Composition and Style Attributes Guided Image Aesthetic Assessment
In this paper we propose a method for the automatic prediction of the aesthetics of an image that is based on the analysis of the semantic content, the artistic style and the composition of the image.
Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework
With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased.
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data.
Learning Combinations of Activation Functions
In the last decade, an active area of research has been devoted to design novel activation functions that are able to help deep neural networks to converge, obtaining better performance.
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks.
Dynamic Graph Convolutional Networks
Many different classification tasks need to manage structured data, which are usually modeled as graphs.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Ranked #2 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
A Cross-Entropy-based Method to Perform Information-based Feature Selection
From a machine learning point of view, identifying a subset of relevant features from a real data set can be useful to improve the results achieved by classification methods and to reduce their time and space complexity.
