arXiv research

Paper proposes GANs for generating business process suffixes and remaining times.

Prediction suffix trees (PST) provide an effective tool for sequence modelling and prediction. Current prediction techniques for PSTs rely on exact matching between the suffix of the current sequence and the previously observed sequence. We present a provably correct algorithm for learning a PST with approximate suffix…

Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTM…

Shorter adversarial prompts help protect LLMs from jailbreak attacks.

We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pretraining or joint optimization with conditional log-likelihood. Given a partial sequence generated by th…

Observer learns optimal policy from learner's actions without rewards.

We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the interaction of an agent with a network---a driver on the transporta…

CROP verifies clean prefixes in reasoning traces, improving downstream repair accuracy.

We consider stochastic gradient descent algorithms for minimizing a non-smooth, strongly-convex function. Several forms of this algorithm, including suffix averaging, are known to achieve the optimal $O(1/T)$ convergence rate in expectation. We consider a simple, non-uniform averaging strategy of Lacoste-Julien et al. …

Ultra-fast search algorithm for trillion-scale corpora with semantic flexibility.

Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are trained in one domain and applied in significantly different domains, their performance can degrade dramatically. We present a methodology f…

Improved shuffling gradient methods converge faster for nonsmooth convex optimization.

SGLBO optimizes quantum circuits with fewer measurements, improving accuracy and noise resilience.

New HMC method handles features in POS tagging, outperforming MEMM.

DKMD is a fast signed statistic for comparing univariate distributions.

Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classical analysis usually required non-trivial smoothness assumptions, which do not apply to many modern applications of SGD with non-smooth object…

Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$ with high probability. We also construct a function from this class for which the error …

Online monitoring system for safety classifiers with shift detection and conformal adaptation

Algorithm learns to bid in auctions with shilling, masking real bids.

Improved reasoning model by sampling from power distribution without additional training.

Simple adaptive attacks can jailbreak state-of-the-art safety-aligned language models.

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.

Plug-and-play multimodal controller improves class-conditional image generation.

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.

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

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,…

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…