Tag: ai/ml

We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fields and camera poses. This latent representation is …

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Like two valedictorians, SimInsights and Photomath tell stories worth hearing about how AI is advancing education. SimInsights in Irvine, Calif., uses NVIDIA conversational AI to make virtual and augmented reality classes lifelike for college students and employee training. Photomath — founded in Zagreb, Croatia and based in San Mateo, Calif. — created an app using …

Read more “Model Teachers: Startups Make Schools Smarter With Machine Learning”

Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task. However, such a speaker independent voice trigger detector typically suffers from performance degradation on …

Read more “Improving Voice Trigger Detection with Metric Learning”

We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene lightings as the Neural Incident Light Field (NeILF) and material properties as the surface BRDF modelled by multi-layer perceptrons. Compared with recent approaches that approximate scene lightings as the 2D environment …

Read more “NeILF: Neural Incident Light Field for Material and Lighting Estimation”

All-neural, end-to-end ASR systems gained rapid interest from the speech recognition community. Such systems convert speech input to text units using a single trainable neural network model. E2E models require large amounts of paired speech text data that is expensive to obtain. The amount of data available varies across different languages and dialects. It is …

Read more “Integrating Categorical Features in End-To-End ASR”

Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size and can accelerate inference, but their relative benefit and combinatorial inter- actions have not been rigorously studied. For each of the eight possible subsets of these techniques, we compare accuracy vs. …

Read more “Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks”

We introduce CVNets, a high-performance open-source library for training deep neural networks for visual recognition tasks, including classification, detection, and segmentation. CVNets supports image and video understanding tools, including data loading, data transformations, novel data sampling methods, and implementations of several standard networks with similar or better performance than previous studies.

Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the large number of frequently-changing named entities. In addition, resources available for recognition are constrained when ASR is performed on-device. In this work, we investigate the use of probabilistic grammars …

Read more “Space-Efficient Representation of Entity-centric Query Language Models”