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Found 9 papers, 7 papers with code
DataComp-LM: In search of the next generation of training sets for language models
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models.
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams
Composable core-sets are core-sets with the property that subsets of the core set can be unioned together to obtain an approximation for the original data; lending themselves to be used for streamed or distributed data.
DeepCurrents: Learning Implicit Representations of Shapes with Boundaries
Recent techniques have been successful in reconstructing surfaces as level sets of learned functions (such as signed distance fields) parameterized by deep neural networks.
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
Task-based distributed frameworks (e. g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving.
Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models
In urban spatial networks, there is an interdependency between neighborhood roles and the transportation methods between neighborhoods.
Social and Information Networks Statistics Theory Adaptation and Self-Organizing Systems Physics and Society Applications Statistics Theory
Learning to Play With Intrinsically-Motivated, Self-Aware Agents
We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.
Training Classifiers with Natural Language Explanations
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification).
Ray: A Distributed Framework for Emerging AI Applications
To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state.
Real-Time Machine Learning: The Missing Pieces
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making.
