omd JAX code for the paper “Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation” Summary Model based reinforcement learning typically trains the dynamics and reward functions by minimizing the error of predictions.The error is only a proxy to maximizing the sum of rewards, the ultimate goal of the agent, leading to the objective mismatch.We propose an end-to-end algorithm called Optimal Model Design (OMD) that optimizes the returns directly for model learning.OMD leverages the implicit function theorem to optimize the model parameters […]

merlot MERLOT is a model for learning what we are calling “neural script knowledge” — representations about what is going on in videos, spanning multiple video frames with associated captions. What’s here We are releasing the following: Code for the MERLOT model (in model/, with data processing in data/ Code for running MERLOT over visual story ordering. We plan to release: Information about the videos used in this work Code for adapting the model to other tasks (not strictly needed, […]

StyleSpeech PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. [x] StyleSpeech (naive branch) [x] Meta-StyleSpeech (main branch) Dependencies You can install the Python dependencies with pip3 install -r requirements.txt Inference You have to download pretrained models and put them in output/ckpt/LibriTTS/. For English single-speaker TTS, run python3 synthesize.py –text “YOUR_DESIRED_TEXT” –ref_audio path/to/reference_audio.wav –restore_step 200000 –mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml The generated utterances will be put in output/result/. Your synthesized speech will have ref_audio‘s style. Batch Inference […]

BCES Python module for performing robust linear regression on (X,Y) data points where both X and Y have measurement errors. The fitting method is the bivariate correlated errors and intrinsic scatter (BCES) and follows the description given in Akritas & Bershady. 1996, ApJ. Some of the advantages of BCES regression compared to ordinary least squares fitting (quoted from Akritas & Bershady 1996): it allows for measurement errors on both variables it permits the measurement errors for the two variables to […]