A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The paper investigates the impact of variations in machine learning training regimes and paradigms on energy consumption. The authors aim to create awareness about the energy implications of general training…
Continue readingThe paper presents Transformer Neural Autoregressive Flows (T-NAFs), a novel solution to challenges in Normalizing Flows (NFs). NFs are a sequence of invertible transformations used in machine learning for density…
Continue readingThe paper by Jon Schneider and Julian Zimmert from Google Research addresses the problem of designing contextual bandit algorithms in cross-learning settings, where the learner observes the loss for the…
Continue readingThe paper presents a systematic study of example difficulty scores, which are used in dataset pruning and defect identification. The authors analyze the consistency of these scores across different training…
Continue readingThe researchers investigate the problem of learning equivariant neural networks through gradient descent. Despite the known symmetries improving the performance of learning pipelines in fields like biology and computer vision,…
Continue readingThe paper discusses the best-of-n policy used for aligning generative models. It disproves a common claim that the KL divergence between the best-of-n policy and the base policy is equal…
Continue readingThe paper introduces a technique for online symbolic music alignment using reinforcement learning. The reinforcement learning agent, an attention-based neural network, is trained to iteratively predict the current score position…
Continue readingThe research paper discusses the use of echolocation clicks emitted by sperm whales for navigation purposes underwater. These clicks serve as a unique pattern, and this study uses the stability…
Continue readingHAAQI-Net is a deep learning model designed to assess music quality for hearing aid users. It uses a Bidirectional Long Short-Term Memory (BLSTM) with attention and takes a music sample…
Continue readingThe paper presents a new text-to-speech (TTS) framework that utilizes a neural transducer. It divides the TTS pipeline into semantic-level sequence-to-sequence modeling and fine-grained acoustic modeling stages. The framework uses…
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