Customer-obsessed science
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December 5, 20256 min readA multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-world inputs.
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November 20, 20254 min read
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October 20, 20254 min read
Featured news
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AAAI 2021 Workshop on DSTC92021Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three tasks: knowledge-seeking turn detection, knowledge selection
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Embedded World Exhibition & Conference 20212021FreeRTOS is a real-time kernel and set of libraries for Internet of Things (IoT) applications. The FreeRTOS kernel provides a portable abstraction layer, task scheduling and interprocess communication (IPC) mechanisms. The main IPC mechanism in FreeRTOS is a concurrent queue: a circular buffer data structure that tasks and interrupt service routines use to exchange messages. As a fundamental building block
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ICML 20212021Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to over-smoothing, long-range dependencies, and spurious edges, e.g., as can occur as a result of graph heterophily or adversarial attacks. To at least partially address these issues within a simple transparent framework, we consider a new family of GNN layers designed
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CVPR 2021 Fourth Workshop on Computer Vision for Fashion, Art and Design2021We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category
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UAI 20212021Meta-learning learns across historical tasks with the goal to discover a representation from which it is easy to adapt to unseen tasks. Episodic meta-learning attempts to simulate a realistic setting by generating a set of small artificial tasks from a larger set of training tasks for meta-training and proceeds in a similar fashion for meta-testing. However, this (meta-)learning paradigm has recently been
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