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Found 6 papers, 5 papers with code
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization
First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning.
Sparse Continuous Distributions and Fenchel-Young Losses
In contrast, for finite domains, recent work on sparse alternatives to softmax (e. g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support.
Multimodal Continuous Visual Attention Mechanisms
Visual attention mechanisms are a key component of neural network models for computer vision.
Sparse and Continuous Attention Mechanisms
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).
Ranked #36 on
Visual Question Answering (VQA)
on VQA v2 test-std
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization
In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections.
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions.
Optimization and Control Information Theory Information Theory
