Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we comprehensively analyze $k$NN-MT through theoretical and empirical studies. Initially, we provide new insights into the working mechanism of $k$NN-MT as an efficient technique to implicitly execute gradient descent on the output projection layer of NMT, indicating that it is a specific case of model fine-tuning. Subsequently, we conduct multi-domain experiments and word-level analysis to examine the differences in performance between $k$NN-MT and entire-model fine-tuning. Our findings suggest that: (1) Incorporating $k$NN-MT with adapters yields comparable translation performance to fine-tuning on in-domain test sets, while achieving better performance on out-of-domain test sets; (2) Fine-tuning significantly outperforms $k$NN-MT on the recall of in-domain low-frequency words, but this gap could be bridged by optimizing the context representations with additional adapter layers.Comment: Accepted by EMNLP202

Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-shot adaptation to unseen tasks. Despite recent advances in meta-RL, most existing methods require the access to the environmental reward function of new tasks to infer the task objective, which is not realistic in many practical applications. To bridge this gap, we study the problem of few-shot adaptation in the context of human-in-the-loop reinforcement learning. We develop a meta-RL algorithm that enables fast policy adaptation with preference-based feedback. The agent can adapt to new tasks by querying human's preference between behavior trajectories instead of using per-step numeric rewards. By extending techniques from information theory, our approach can design query sequences to maximize the information gain from human interactions while tolerating the inherent error of non-expert human oracle. In experiments, we extensively evaluate our method, Adaptation with Noisy OracLE (ANOLE), on a variety of meta-RL benchmark tasks and demonstrate substantial improvement over baseline algorithms in terms of both feedback efficiency and error tolerance.Comment: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

Surrogate-assisted reliability-based design optimization: a survey and a new general framework

International audienc

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants

An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach

This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package. © 2019 Ain Shams UniversityUniversidad Nacional de Colombia, UN: 38945, 58838 P17211 Universidad Tecnológica de Pereira, UTP: C2019P011, C2018P020 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS: 727-2015This work was funded in part by the Administrative Department of Science, Technology, and Innovation of Colombia (COLCIENCIAS) through its National Scholarship Program, under Grant 727-2015 ; in part by Instituto Tecnológico Metropolitano de Medellín, under Project P17211; in part by Universidad Tecnológica de Bolívar, under Projects C2018P020 and C2019P011; and in part by Universidad Nacional de Colombia, under Proyect ”Estrategia de transformación del sector energético Colombiano en el horizonte de 2030 - Energética 2030” - ”Generación distribuida de energía eléctrica en Colombia a partir de energía solar y eólica” (Code: 58838, Hermes: 38945). Oscar D. Montoya received his BEE, M.Sc. and Ph.D degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2012 and 2014 respectively. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Walter Gil-González received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2011 and 2013 respectively. He is currently studying a Ph.D in Electrical Engineering at Universidad Tecnológica de Pereira, Colombia. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Luis F. Grisales received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2013 and 2015 respectively. He is currently studying a Ph.D in Engineering at Universidad Nacional de Colombia. Actually, is professor in the Instituto TecnolÓgico Metropolitano de Medellín, attached to the Department of Electromechanics and mechatronics, member of the research group MATyER. His research interests include mathematical modelling, optimization techniques, planning and control of power systems, renewable energies, energy storage, power electronic and smartgrids

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organized, self- managed and self-monitoring applications with ability to analyze and predict both the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for IoT applications in industry 5.0. Knowledge- Defined Networks (KDN) bridges this gap by extending SDN architecture with Knowledge Plane (KP) which learns the net- work dynamics to avoid sub-optimal decisions. Cognitive Routing leverages the Sixth-Generation (6G) Self-Organised-Networks with self-learning feature. This paper presents a self-organized cognitive routing frame- work for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most-reliable path with minimal probability of route-flapping. The proposed framework pre-calculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry stranded Link- state and distance-vector routing algorithms SPF and DUAL respectively