Decoding of Polar Codes Using Quadratic Unconstrained Binary Optimization
Huayi Zhou
Ryan Seah
Marwan Jalaleddine
Warren Gross
2024-01-01
IEEE Journal on Selected Areas in Communications (published)
doi.org
Deep Learning Approach for Changepoint Detection: Penalty Parameter Optimization
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection, a technique for identifying significant shifts within data sequences, is crucial in various fields such as finance, g… (see more)enomics, medicine, etc. Dynamic programming changepoint detection algorithms are employed to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, previous work uses simple models such as linear models or decision trees. This study introduces a novel deep learning method for predicting penalty parameters, leading to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets compared to previous methods.
2024-01-01
arXiv.org (preprint)
doi.org
Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Audrey Durand
Michael Morin
Philippe Marier
Jonathan Gaudreault
2024-01-01
Computers in industry (Print) (published)
doi.org
Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Audrey Durand
Michael Morin
Philippe Marier
Jonathan Gaudreault
2024-01-01
Computers in Industry (published)
doi.org
In deep reinforcement learning, a pruned network is a good network
Johan Samir Obando Ceron
Aaron Courville
Pablo Samuel Castro
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (see more)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks and exhibit a type of"scaling law", using only a small fraction of the full network parameters.
2024-01-01
ICML (published)
doi.org
arxiv.org
Designing and Evaluating Dialogue LLMs for Co-Creative Improvised Theatre
Boyd Branch
Piotr Mirowski
Kory Wallace Mathewson
Sophia Ppali
Alexandra Covaci
Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world e… (see more)valuations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.
2024-01-01
ICCC (published)
doi.org
arxiv.org
On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (see more)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.
2024-01-01
NeurIPS (published)
doi.org
arxiv.org
Discovering modular solutions that generalize compositionally
Simon Schug
Seijin Kobayashi
Yassir Akram
Maciej Wolczyk
Alexandra Proca
Johannes Von Oswald
Razvan Pascanu
João Sacramento
Angelika Steger
Many complex tasks can be decomposed into simpler, independent parts. Discovering such underlying compositional structure has the potential … (see more)to enable compositional generalization. Despite progress, our most powerful systems struggle to compose flexibly. It therefore seems natural to make models more modular to help capture the compositional nature of many tasks. However, it is unclear under which circumstances modular systems can discover hidden compositional structure. To shed light on this question, we study a teacher-student setting with a modular teacher where we have full control over the composition of ground truth modules. This allows us to relate the problem of compositional generalization to that of identification of the underlying modules. In particular we study modularity in hypernetworks representing a general class of multiplicative interactions. We show theoretically that identification up to linear transformation purely from demonstrations is possible without having to learn an exponential number of module combinations. We further demonstrate empirically that under the theoretically identified conditions, meta-learning from finite data can discover modular policies that generalize compositionally in a number of complex environments.
2024-01-01
International Conference on Learning Representations (published)
doi.org
arxiv.org
Disentangling the Causes of Plasticity Loss in Neural Networks
Clare Lyle
Zeyu Zheng
Khimya Khetarpal
Hado van Hasselt
Razvan Pascanu
James Martens
Will Dabney
2024-01-01
CoLLAs (published)
doi.org
arxiv.org
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence.
Marcel Hussing
Claas Voelcker
Igor Gilitschenski
Amir-massoud Farahmand
Eric R. Eaton
2024-01-01
RLJ (published)
arxiv.org
arxiv.org
Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Litao Li
Steven H. H. Ding
Andrew Walenstein
Philippe Charland
Benjamin Fung
2024-01-01
CIKM (published)
doi.org
arxiv.org
E(3)-Equivariant Mesh Neural Networks
Thuan Nguyen Anh Trang
Khang Nhat Ngo
Daniel Levy
Thieu Vo
Siamak Ravanbakhsh
Truong Son Hy
Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have addressed the need for geometr… (see more)ic deep learning on 3D meshes. However, we observe that the complexities in many of these architectures do not translate to practical performance, and simple deep models for geometric graphs are competitive in practice. Motivated by this observation, we minimally extend the update equations of E(n)-Equivariant Graph Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face information and further improve it to account for long-range interactions through a hierarchy. The resulting architecture, Equivariant Mesh Neural Network (EMNN), outperforms other, more complicated equivariant methods on mesh tasks, with a fast run-time and no expensive preprocessing. Our implementation is available at https://github.com/HySonLab/EquiMesh.
2024-01-01
AISTATS (published)
doi.org
arxiv.org