Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by being invariant to data augmentations. Given this limitation, there has been a surge of interest in equivariant self-supervised learning (E-SSL) that learns features to be augmentation-aware. However, even for the simplest rotation prediction method, there is a lack of rigorous understanding of why, when, and how E-SSL learns useful features for downstream tasks. To bridge this gap between practice and theory, we establish an information-theoretic perspective to understand the generalization ability of E-SSL. In particular, we identify a critical explaining-away effect in E-SSL that creates a synergy between the equivariant and classification tasks. This synergy effect encourages models to extract class-relevant features to improve its equivariant prediction, which, in turn, benefits downstream tasks requiring semantic features. Based on this perspective, we theoretically analyze the influence of data transformations and reveal several principles for practical designs of E-SSL.

Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by being invariant to data augmentations. Given this limitation, there has been a surge of interest in equivariant self-supervised learning (E-SSL) that learns features to be augmentation-aware. However, even for the simplest rotation prediction method, there is a lack of rigorous understanding of why, when, and how E-SSL learns useful features for downstream tasks. To bridge this gap between practice and theory, we establish an information-theoretic perspective to understand the generalization ability of E-SSL. In particular, we identify a critical explaining-away effect in E-SSL that creates a synergy between the equivariant and classification tasks.

In multi-task reinforcement learning (MTRL), the objective is to simultaneously learn multiple tasks and exploit their similarity to improve the performance w.r.t.

Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization.

In multi-task reinforcement learning (MTRL), the objective is to simultaneously learn multiple tasks and exploit their similarity to improve the performance w.r.t.

Public transportation has been essential in people's lives in recent years. Bus ridership is a factor in people's choice to board the bus. Therefore, from the perspective of improving service quality, it is important to inform passengers who have not boarded the bus yet about future bus ridership. However, there is a concern that providing inaccurate information may cause a negative experience. Against this backdrop, there is a need to provide bus passengers who have not boarded yet with highly accurate predictions. Many researchers are working on studies on this. However, two issues summarize related studies. The first is that the correlation of bus ridership between consecutive bus stops should be considered for the prediction. The second is that the prediction has yet to be made using all of the features shown to be useful in each related study. This study proposes a prediction method that addresses both of these issues. We solve the first issue by designing an LSTM-based architecture for each bus stop and a single model for the entire bus stop. We solve the second issue by inputting all useful data, the past bus ridership, day of the week, time section, weather, and precipitation, as features. Bus ridership at each bus stop collected from buses operated by Minato Kanko Bus Inc, in Kobe city, Hyogo, Japan, from October 1, 2021, to September 30, 2022, were used to compare accuracy. The proposed method improved RMSE by 23% on average and up to 27% compared to existing methods.

To start building an ML Platform, you should support the basic ML user journey of notebook prototyping to scaled training to online serving. If your organization has multiple teams, you may additionally need to support administrative requirements of multi-user support with identity-based authentication and authorization. Two popular OSS projects – Kubeflow and Ray – together can support these needs. Kubeflow provides the multi-user environment and interactive notebook management. Ray orchestrates distributed computing workloads across the entire ML lifecycle, including training and serving.

Traditionally, feature engineering has been a crucial step in the machine learning process, where raw data is transformed into useful features that can be used to train models. However, with the advent of deep learning, neural networks have the ability to learn useful features from the raw data automatically, without the need for manual feature engineering. In this article, we will explore how deep learning approaches feature engineering and how it can improve the performance of machine learning models. One of the key benefits of deep learning is its ability to automatically learn useful features from raw data. This is achieved through the use of layers of artificial neurons, which are trained to extract features from the input data at each layer. For example, a convolutional neural network (CNN) uses a series of convolutional layers to extract features from images, such as edges, textures, and shapes.

If you are looking for a smart home hub and security camera, consider the Aqara Camera Hub G3. It uses artificial intelligence and video to monitor a room, and its powerful sensor can capture razor-sharp video. It even has a night vision mode for night-time surveillance. The Amazon Echo Show 15 offers a bulletin-board style display with the familiar Alexa host. The screen can be used for organizing your household by adding appointments to your calendar, items to your shopping list, or a homework reminder. You can control your home devices with it and make voice chats with your family.