391 research outputs found
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Evolutionary neural architecture search for deep learning
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains.
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used.
While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs.
This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters.
It builds upon extensive past research of evolutionary optimization of neural network structure.
Various improvements to the core algorithm are introduced, including:
(1) discovering DNN architectures of arbitrary complexity;
(1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs;
(3) extending to the multitask learning and multiobjective optimization domains;
(4) maximizing performance and reducing wasted computation through asynchronous evaluations.
Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks.
Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks.Computer Science
Crossmodal Attentive Skill Learner
This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated
with the recently-introduced Asynchronous Advantage Option-Critic (A2OC)
architecture [Harb et al., 2017] to enable hierarchical reinforcement learning
across multiple sensory inputs. We provide concrete examples where the approach
not only improves performance in a single task, but accelerates transfer to new
tasks. We demonstrate the attention mechanism anticipates and identifies useful
latent features, while filtering irrelevant sensor modalities during execution.
We modify the Arcade Learning Environment [Bellemare et al., 2013] to support
audio queries, and conduct evaluations of crossmodal learning in the Atari 2600
game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017],
we open-source a fast hybrid CPU-GPU implementation of CASL.Comment: International Conference on Autonomous Agents and Multiagent Systems
(AAMAS) 2018, NIPS 2017 Deep Reinforcement Learning Symposiu
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Foundation models are first pre-trained on vast unsupervised datasets and
then fine-tuned on labeled data. Reinforcement learning, notably from human
feedback (RLHF), can further align the network with the intended usage. Yet the
imperfections in the proxy reward may hinder the training and lead to
suboptimal results; the diversity of objectives in real-world tasks and human
opinions exacerbate the issue. This paper proposes embracing the heterogeneity
of diverse rewards by following a multi-policy strategy. Rather than focusing
on a single a priori reward, we aim for Pareto-optimal generalization across
the entire space of preferences. To this end, we propose rewarded soup, first
specializing multiple networks independently (one for each proxy reward) and
then interpolating their weights linearly. This succeeds empirically because we
show that the weights remain linearly connected when fine-tuned on diverse
rewards from a shared pre-trained initialization. We demonstrate the
effectiveness of our approach for text-to-text (summarization, Q&A, helpful
assistant, review), text-image (image captioning, text-to-image generation,
visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the
alignment of deep models, and how they interact with the world in all its
diversity
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