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Found 5 papers, 3 papers with code
What to Do When Your Discrete Optimization Is the Size of a Neural Network?
Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and training of binary networks.
Floralens: a Deep Learning Model for the Portuguese Native Flora
Machine-learning techniques, namely deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms.
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios
MONET is different from previous thermal drone datasets because it features multimodal data, including rural scenes captured with thermal cameras containing both person and vehicle targets, along with trajectory information and metadata.
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification.
Winning the Lottery with Continuous Sparsification
Additionally, the recent Lottery Ticket Hypothesis conjectures that, for a typically-sized neural network, it is possible to find small sub-networks which, when trained from scratch on a comparable budget, match the performance of the original dense counterpart.
