Search Results for author: Yashas Annadani

Found 8 papers, 2 papers with code

Interventions, Where and How? Experimental Design for Causal Models at Scale

no code implementations3 Mar 2022 Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer

Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability which introduces uncertainties in estimating the underlying structural causal model (SCM).

Causal Discovery Experimental Design

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Structure by Architecture: Disentangled Representations without Regularization

no code implementations14 Jun 2020 Felix Leeb, Guilia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

We study the problem of self-supervised structured representation learning using autoencoders for generative modeling.

Disentanglement

Noise Contrastive Variational Autoencoders

no code implementations23 Jul 2019 Octavian-Eugen Ganea, Yashas Annadani, Gary Bécigneul

We take steps towards understanding the "posterior collapse (PC)" difficulty in variational autoencoders (VAEs),~i. e.

Preserving Semantic Relations for Zero-Shot Learning

no code implementations CVPR 2018 Yashas Annadani, Soma Biswas

We devise objective functions to preserve these relations in the embedding space, thereby inducing semanticity to the embedding space.

Zero-Shot Learning

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

no code implementations14 Nov 2016 Yashas Annadani, Vijayakrishna Naganoor, Akshay Kumar Jagadish, Krishnan Chemmangat

We investigate and analyse the performance of popular CNN architectures (GoogleNet, AlexNet), used for other image classification tasks, when subjected to the task of detecting the selfies on the multimedia platform.

Image Classification

Sliding Dictionary Based Sparse Representation For Action Recognition

no code implementations1 Nov 2016 Yashas Annadani, D L Rakshith, Soma Biswas

This is used to compute the sparse coefficients of the input action sequence which is divided into overlapping windows and each window gives a probability score for each action class.

Action Recognition

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