Search Results for author:
Found 9 papers, 4 papers with code
Attentive Semantic Video Generation using Captions
This paper proposes a network architecture to perform variable length semantic video generation using captions.
Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW).
UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation
We introduce UPS (Unified PDE Solver), an effective and data-efficient approach to solve diverse spatiotemporal PDEs defined over various domains, dimensions, and resolutions.
Disentangling the Mechanisms Behind Implicit Regularization in SGD
A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training.
Interactive Image Generation Using Scene Graphs
We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs).
Sentiment Dynamics in Social Media News Channels
We also analyze users' reactions and opinion sentiment on news posts with different sentiments.
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Recent experiments have shown that deep networks can approximate solutions to high-dimensional PDEs, seemingly escaping the curse of dimensionality.
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
We show that if composing a function with Barron norm $b$ with partial derivatives of $L$ produces a function of Barron norm at most $B_L b^p$, the solution to the PDE can be $\epsilon$-approximated in the $L^2$ sense by a function with Barron norm $O\left(\left(dB_L\right)^{\max\{p \log(1/ \epsilon), p^{\log(1/\epsilon)}\}}\right)$.
Deep Equilibrium Based Neural Operators for Steady-State PDEs
Motivated by this observation, we propose FNO-DEQ, a deep equilibrium variant of the FNO architecture that directly solves for the solution of a steady-state PDE as the infinite-depth fixed point of an implicit operator layer using a black-box root solver and differentiates analytically through this fixed point resulting in $\mathcal{O}(1)$ training memory.
