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Found 10 papers, 9 papers with code
[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation
This paper focuses on reproducing and extending the results of the paper: "Modeling Personalized Item Frequency Information for Next-basket Recommendation" which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR).
Hitting "Probe"rty with Non-Linearity, and More
Additionally, we also use it to qualitatively investigate the structure of dependency trees that BERT encodes in each of its layers.
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
Implicit layer deep learning techniques, like Neural Differential Equations, have become an important modeling framework due to their ability to adapt to new problems automatically.
Continuous Deep Equilibrium Models: Training Neural ODEs faster by integrating them to Infinity
Additionally, we address the question: is there a way to simultaneously achieve the robustness of implicit layers while allowing the reduced computational expense of an explicit layer?
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
While we can control the computational cost by choosing the number of layers in standard architectures, in NDEs the number of neural network evaluations for a forward pass can depend on the number of steps of the adaptive ODE solver.
Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness
Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence.
Emergent Road Rules In Multi-Agent Driving Environments
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow.
TorchGAN: A Flexible Framework for GAN Training and Evaluation
The key features of TorchGAN are its extensibility, built-in support for a large number of popular models, losses and evaluation metrics, and zero overhead compared to vanilla PyTorch.
RayTracer.jl: A Differentiable Renderer that supports Parameter Optimization for Scene Reconstruction
In this paper, we present RayTracer. jl, a renderer in Julia that is fully differentiable using source-to-source Automatic Differentiation (AD).
Fashionable Modelling with Flux
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities.
