Search Results for author:
Found 18 papers, 8 papers with code
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
The "something something" video database for learning and evaluating visual common sense
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
Ranked #115 on
Action Recognition
on Something-Something V2
Towards Deep Conversational Recommendations
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems.
FigureQA: An Annotated Figure Dataset for Visual Reasoning
To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.
Ranked #3 on
Visual Question Answering (VQA)
on FigureQA - test 1
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis.
Learning Robust Dynamics through Variational Sparse Gating
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes.
ChatPainter: Improving Text to Image Generation using Dialogue
Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task.
Ranked #25 on
Text-to-Image Generation
on MS COCO
(Inception score metric)
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
RATM: Recurrent Attentive Tracking Model
The proposed Recurrent Attentive Tracking Model performs well on all three tasks and can generalize to related but previously unseen sequences from a challenging tracking data set.
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.
Modeling sequential data using higher-order relational features and predictive training
In this work we extend bi-linear models by introducing "higher-order mapping units" that allow us to encode transformations between frames and transformations between transformations.
Learning to encode motion using spatio-temporal synchrony
To this end, we show that the detection of spatial transformations can be viewed as the detection of synchrony between the image sequence and a sequence of features undergoing the motion we wish to detect.
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""
We propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1.
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep neural networks.
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.
