Decomposable Transformer Point Processes

• URL: http://arxiv.org/abs/2409.18158v1
• Date: Thu, 26 Sep 2024 13:22:58 GMT
• Title: Decomposable Transformer Point Processes
• Authors: Aristeidis Panos,
• Abstract summary: We propose a framework where the advantages of the attention-based architecture are maintained and the limitation of the thinning algorithm is circumvented. The proposed method attains state-of-the-art performance in predicting the next event of a sequence given its history.
• Score: 2.1756081703276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The standard paradigm of modeling marked point processes is by parameterizing the intensity function using an attention-based (Transformer-style) architecture. Despite the flexibility of these methods, their inference is based on the computationally intensive thinning algorithm. In this work, we propose a framework where the advantages of the attention-based architecture are maintained and the limitation of the thinning algorithm is circumvented. The framework depends on modeling the conditional distribution of inter-event times with a mixture of log-normals satisfying a Markov property and the conditional probability mass function for the marks with a Transformer-based architecture. The proposed method attains state-of-the-art performance in predicting the next event of a sequence given its history. The experiments also reveal the efficacy of the methods that do not rely on the thinning algorithm during inference over the ones they do. Finally, we test our method on the challenging long-horizon prediction task and find that it outperforms a baseline developed specifically for tackling this task; importantly, inference requires just a fraction of time compared to the thinning-based baseline.

Related papers

• The Stochastic Conjugate Subgradient Algorithm For Kernel Support Vector Machines [1.738375118265695]
  This paper proposes an innovative method specifically designed for kernel support vector machines (SVMs) It not only achieves faster iteration per iteration but also exhibits enhanced convergence when compared to conventional SFO techniques. Our experimental results demonstrate that the proposed algorithm not only maintains but potentially exceeds the scalability of SFO methods.
  arXiv  Detail & Related papers  (2024-07-30T17:03:19Z)
• Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search [8.226785409557598]
  Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. In this paper, we aim to achieve two improvements in QAS: (1) to reduce the number of measurements by an online surrogate model of the evaluation process that aggressively discards architectures of poor performance; (2) to avoid training the circuits when BPs are present. We experimentally validate our proposal for the variational quantum eigensolver and showcase that our algorithm is able to find solutions that have been previously proposed in the literature for the Hamiltonians; but also to outperform the state of the
  arXiv  Detail & Related papers  (2024-07-29T15:22:39Z)
• Provably Efficient Learning in Partially Observable Contextual Bandit [4.910658441596583]
  We show how causal bounds can be applied to improving classical bandit algorithms. This research has the potential to enhance the performance of contextual bandit agents in real-world applications.
  arXiv  Detail & Related papers  (2023-08-07T13:24:50Z)
• Diffusion Action Segmentation [63.061058214427085]
  We propose a novel framework via denoising diffusion models, which shares the same inherent spirit of such iterative refinement. In this framework, action predictions are iteratively generated from random noise with input video features as conditions.
  arXiv  Detail & Related papers  (2023-03-31T10:53:24Z)
• Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation [88.14365009076907]
  Iterative refinement is a useful paradigm for representation learning. We develop an implicit differentiation approach that improves the stability and tractability of training.
  arXiv  Detail & Related papers  (2022-07-02T10:00:35Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery [16.740247586153085]
  We show how to leverage gradient-based learning to solve discrete optimization problems. Our approach is formalized by GLODISMO (Gradient-based Learning of DIscrete Structured Measurement Operators) We empirically demonstrate the performance and flexibility of GLODISMO in several signal recovery applications.
  arXiv  Detail & Related papers  (2022-02-07T18:27:08Z)
• Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection [7.685002911021767]
  We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses.
  arXiv  Detail & Related papers  (2021-05-20T01:57:52Z)
• Active Model Estimation in Markov Decision Processes [108.46146218973189]
  We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP) We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
  arXiv  Detail & Related papers  (2020-03-06T16:17:24Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.