Search Results for author: Harald Steck

Found 8 papers, 4 papers with code

Is Cosine-Similarity of Embeddings Really About Similarity?

no code implementations8 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.

Semantic Similarity Semantic Textual Similarity

Large Language Models as Zero-Shot Conversational Recommenders

1 code implementation19 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.

On the Regularization of Autoencoders

no code implementations21 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.

Autoencoders that don't overfit towards the Identity

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.

Denoising

Markov Random Fields for Collaborative Filtering

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).

Collaborative Filtering

Embarrassingly Shallow Autoencoders for Sparse Data

7 code implementations8 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.

Collaborative Filtering Recommendation Systems

Collaborative Filtering via High-Dimensional Regression

no code implementations30 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.

Collaborative Filtering regression +1

On Ranking in Survival Analysis: Bounds on the Concordance Index

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.

Survival Analysis

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