no code implementations • 8 Mar 2024 • Harald Steck, Chaitanya Ekanadham, Nathan Kallus
Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations.
1 code implementation • 19 Aug 2023 • Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
no code implementations • 21 Oct 2021 • Harald Steck, Dario Garcia Garcia
While much work has been devoted to understanding the implicit (and explicit) regularization of deep nonlinear networks in the supervised setting, this paper focuses on unsupervised learning, i. e., autoencoders are trained with the objective of reproducing the output from the input.
1 code implementation • NeurIPS 2020 • Harald Steck
In this paper, we consider linear autoencoders, as they facilitate analytic solutions, and first show that denoising / dropout actually prevents the overfitting towards the identity-function only to the degree that it is penalized by the induced L2-norm regularization.
1 code implementation • NeurIPS 2019 • Harald Steck
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF).
7 code implementations • 8 May 2019 • Harald Steck
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems.
Ranked #1 on Recommendation Systems on Million Song Dataset
no code implementations • 30 Apr 2019 • Harald Steck
While the SLIM approach obtained high ranking-accuracy in many experiments in the literature, it is also known for its high computational cost of learning its parameters from data.
no code implementations • NeurIPS 2007 • Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin, Vikas C. Raykar
In contrast, the standard approach to \emph{learning} the popular proportional hazard (PH) model is based on Cox's partial likelihood.