Search Results for author:
Found 13 papers, 8 papers with code
Learning Natural Language Generation with Truncated Reinforcement Learning
To our knowledge, it is the first approach that successfully learns a language generation policy without pre-training, using only reinforcement learning.
Diffusion bridges vector quantized Variational AutoEncoders
In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant $\mathrm{Z}$ are challenging problems.
Learning Natural Language Generation from Scratch
This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement learning (RL).
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform
Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant Z are challenging problems.
Joint self-supervised blind denoising and noise estimation
Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data.
CoRe: Color Regression for Multicolor Fashion Garments
That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture.
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture.
Insights from the Future for Continual Learning
Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting.
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning.
DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine
The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy.
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation
Similarly to self-training methods, the predictions of these initial detectors mitigate the missing annotations on the complementary datasets.
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
In this paper, we present a method to learn a visual representation adapted for e-commerce products.
