arXiv research

Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new us…

Paper presents a reinforcement learning framework for personalized music playlist generation.

Study compares methods for treatment assignment, finding A-learner best for playlist generation.

We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to be obscure, long-tail artists and generally have little or no available user preference data associ…

The paper tackles carousel personalization in music streaming apps using contextual bandits.

Generative model learns to create coherent slates from prompts.

South Indian classical music (Carnatic music) is best consumed through live concerts. A carnatic recital requires meticulous planning accounting for several parameters like the performers' repertoire, composition variety, musical versatility, thematic structure, the recital's arrangement, etc. to ensure that the audien…

This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using …

Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has sugge…

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ,…

PenduMAV is a 6-input omnidirectional MAV without internal forces.

Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances of speech can be turned into "illusions" which either alter hum…

CRL improves recommendation systems by reducing distribution shift.

Paper defines generalized braids and proves their subgroup status.

Defines a new Poisson structure for generalized Sasakian spaces.

Improved image generation through iterative flow matching to reduce hallucinations.

Framework generates personalized insulin treatment strategies using deep models.

OptiGAN uses GAN and RL to optimize sequence generation for specific goals.

Survey on deep models for graph generation.

Improves deep generative models to generate images of any size.

Develops a unified theory of Yang-Mills and GR using generalized principal bundles.

Meta-CoTGAN improves adversarial text generation by preventing mode collapse.

Generative models can still learn from contaminated data, but with limitations.

Generative AI tasks analyzed for text, images, audio, video, code, and molecules.

Defines Kahler angle for a broader context.

Established a generalized Boothby-Wang theorem in contact geometry.

The twistor construction for Riemannian manifolds is extended to the case of manifolds endowed with generalized metrics (in the sense of generalized geometry à la Hitchin). The generalized twistor space associated to such a manifold is defined as the bundle of generalized complex structures on the tangent spaces of the…

We present a characterization, in terms of torsion-free generalized connections, for the integrability of various generalized structures (generalized almost complex structures, generalized almost hypercomplex structures, generalized almost Hermitian structures and generalized almost hyper-Hermitian structures) defined …

This research proves guarantees on sequence models' generalization to longer and novel sequences.

SHADOWCAST generates graphs with user-specified attributes.

In this paper we define the notion of a generalized coKähler structure and prove that the product $M_{1}\times M_{2}$ of generalized contact metric manifolds $(M_i, Φ_i,E_{\pm,i}, G_i)$, $i=1, 2$, where $M_{1}\times M_{2}$ is endowed with the product generalized complex structure induced from $Φ_1$ and $Φ_2$, is gener…

We define the generalized connected sum for generic closed plane curves, generalizing the strange sum defined by Arnold, and completely describe how the Arnold invariants $J^{\pm}$ and $\mathit{St}$ behave under the generalized connected sums.

In this communication, we describe some interrelations between generalized $q$-entropies and a generalized version of Fisher information. In information theory, the de Bruijn identity links the Fisher information and the derivative of the entropy. We show that this identity can be extended to generalized versions of en…

A new method generates graphs with hierarchical structures.

The paper extends symplectic techniques to generalized complex geometry.

We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence. However, in the context of a story,…

A new method for generating sets and graphs without requiring exchangeability.

DeepWeightFlow generates diverse neural network weights efficiently.

Generalized tensor analysis in the sense of Colombeau's construction is employed to introduce a nonlinear distributional pseudo-Riemannian geometry. In particular, after deriving several characterizations of invertibility in the algebra of generalized functions we define the notions of generalized pseudo-Riemannian met…

It is known that the scalar curvature arises as the moment map in Kahler geometry. In pursuit of this analogy, we introduce the notion of a moment map in generalized Kahler geometry which gives the definition of a generalized scalar curvature on a generalized Kahler manifold. From the viewpoint of the moment map, we ob…

This is an expository paper, which provides a first introduction to geometric structures on $TM\oplus T^*M$. The paper contains definitions and characteristic properties of generalized complex, generalized Kaehler, generalized (normal, almost) contact and generalized Sasakian structures. A few of these properties are n…

We characterize language generation with stability and breadth, proving impossibility results.

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any ad…

Rate GENERIC extends thermodynamics principles to non-equilibrium systems.

The paper defines MTCov for skewed elliptical distributions.

The notion of a generalized Lie bialgebroid (a generalization of the notion of a Lie bialgebroid) is introduced in such a way that a Jacobi manifold has associated a canonical generalized Lie bialgebroid. As a kind of converse, we prove that a Jacobi structure can be defined on the base space of a generalized Lie bialg…

QGAA learns latent quantum states, reducing errors in quantum data generation.

Sharpness minimization algorithms don't solely improve generalization.