Search Results for author: Armin Karamzade

Found 5 papers, 1 papers with code

Reinforcement Learning from Delayed Observations via World Models

no code implementations18 Mar 2024 Armin Karamzade, KyungMin Kim, Montek Kalsi, Roy Fox

In standard Reinforcement Learning settings, agents typically assume immediate feedback about the effects of their actions after taking them.


Moonwalk: Inverse-Forward Differentiation

no code implementations22 Feb 2024 Dmitrii Krylov, Armin Karamzade, Roy Fox

Our method, Moonwalk, has a time complexity linear in the depth of the network, unlike the quadratic time complexity of na\"ive forward, and empirically reduces computation time by several orders of magnitude without allocating more memory.

Matching DNN Compression and Cooperative Training with Resources and Data Availability

no code implementations2 Dec 2022 Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade, Marco Levorato, Carla Fabiana Chiasserini

To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance.

Regularizing Recurrent Neural Networks via Sequence Mixup

no code implementations27 Nov 2020 Armin Karamzade, Amir Najafi, Seyed Abolfazl Motahari

In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold Mixup (Verma et al., 2018), to the realm of Recurrent Neural Networks (RNN).

named-entity-recognition Named Entity Recognition +1

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