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Most implemented papers

Image Inpainting for Irregular Holes Using Partial Convolutions

NVIDIA/partialconv ECCV 2018

Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).

Free-Form Image Inpainting with Gated Convolution

JiahuiYu/generative_inpainting ICCV 2019

We present a generative image inpainting system to complete images with free-form mask and guidance.

Using the Output Embedding to Improve Language Models

ofirpress/UsingTheOutputEmbedding EACL 2017

We study the topmost weight matrix of neural network language models.

MolGAN: An implicit generative model for small molecular graphs

nicola-decao/MolGAN 30 May 2018

Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures.

The Double Sphere Camera Model

ethz-asl/kalibr 24 Jul 2018

We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.

Phase-aware Speech Enhancement with Deep Complex U-Net

AppleHolic/source_separation ICLR 2019

Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

shehzaadzd/MINERVA ICLR 2018

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.

Multi-Task Learning as Multi-Objective Optimization

IntelVCL/MultiObjectiveOptimization NeurIPS 2018

These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

IBM/causallib 14 Oct 2015

Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity.

Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context

ucdavisnlp/damd-multiwoz 24 Nov 2019

Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context.