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Year Published

172 Results

December 4, 2017

Gradient Episodic Memory for Continual Learning

Neural Information Processing Systems (NIPS)

One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, once and one by one, examples concerning a sequence of tasks.

By: David Lopez-Paz, Marc'Aurelio Ranzato
December 4, 2017

Poincaré Embeddings for Learning Hierarchical Representations

Neural Information Processing Systems (NIPS)

In this work, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space – or more precisely into an n-dimensional Poincaré ball.

By: Maximilian Nickel, Douwe Kiela
December 4, 2017

Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples

Neural Information Processing Systems (NIPS)

We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable.

By: Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet
December 4, 2017

Attentive Explanations: Justifying Decisions and Pointing to the Evidence

Interpretable Machine Learning Symposium at NIPS

In this work, we emphasize the importance of model explanation in various forms such as visual pointing and textual justification.

By: Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
December 4, 2017

On the Optimization Landscape of Tensor Decompositions

Neural Information Processing Systems (NIPS)

In this paper, we analyze the optimization landscape of the random over-complete tensor decomposition problem, which has many applications in unsupervised learning, especially in learning latent variable models. In practice, it can be efficiently solved by gradient ascent on a non-convex objective.

By: Rong Ge, Tengyu Ma
October 24, 2017

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

AAAI Conference on Human Computation and Crowdsourcing (HCOMP)

In this work, we design a cooperative game – GuessWhich – to measure human-AI team performance in the specific context of the AI being a visual conversational agent.

By: Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
October 22, 2017

Segmentation-Aware Convolutional Networks using Local Attention Masks

International Conference on Computer Vision (ICCV)

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision.

By: Adam W. Harley, Konstantinos G. Derpanis, Iasonas Kokkinos
October 22, 2017

Focal Loss for Dense Object Detection

International Conference on Computer Vision (ICCV)

In this paper, we investigate why one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. We design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.

By: Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar
October 22, 2017

Low-shot Visual Recognition by Shrinking and Hallucinating Features

International Conference on Computer Vision (ICCV)

Low-shot visual learning—the ability to recognize novel object categories from very few examples—is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. We present a lowshot learning benchmark on complex images that mimics challenges faced by recognition systems in the wild.

By: Bharath Hariharan, Ross Girshick
October 22, 2017

Dense and Low-Rank Gaussian CRFs using Deep Embeddings

International Conference on Computer Vision (ICCV)

In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure.

By: Siddhartha Chandra, Nicolas Usunier, Iasonas Kokkinos