Transferring Industrial Robot Assembly Tasks from Simulation to Reality
Simulation is an essential tool for robots learning new skills. These skills include perception (understanding the world from camera images), planning...
Towards Environment-specific Base Stations: AI/ML-driven Neural 5G NR Multi-user MIMO Receiver
At this year’s Mobile World Congress (MWC), NVIDIA showcased a neural receiver for a 5G New Radio (NR) uplink multi-user MIMO scenario, which could be seen...
AutoDMP Optimizes Macro Placement for Chip Design with AI and GPUs
Most modern digital chips integrate large numbers of macros in the form of memory blocks or analog blocks, like clock generators. These macros are often much...
Introducing NVIDIA Aerial Research Cloud for Innovations in 5G and 6G
NVIDIA introduced Aerial Research Cloud, the first fully programmable 5G and 6G network research sandbox, which enables researchers to rapidly simulate,...
Turn 2D Images into Immersive 3D Scenes with NVIDIA Instant NeRF in VR
Thousands of developers and content creators have built stunning 3D visuals with NVIDIA Instant NeRF, a rendering tool that turns a set of static images into a...
Marking a year of new and evolving technologies, 2022 produced wide-ranging advancements and AI-powered solutions across industries. These include boosting HPC...
Generating Collision-Free Robot Movement with Motion Policy Networks
Collision-free motion generation in unknown environments is a core building block for robotic applications. Generating such motions is challenging. The motion...
AI Models Recap: Scalable Pretrained Models Across Industries
The year 2022 has thus far been a momentous, thrilling, and an overwhelming year for AI aficionados. Get3D is pushing the boundaries of generative 3D modeling,...
A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.
Text Normalization and Inverse Text Normalization with NVIDIA NeMo
Text normalization (TN) converts text from written form into its verbalized form, and it is an essential preprocessing step before text-to-speech (TTS). TN...
Dynamic Scale Weighting Through Multiscale Speaker Diarization
Speaker diarization is the process of segmenting audio recordings by speaker labels and aims to answer the question “Who spoke when?”. It makes a clear...
Improving Japanese Language ASR by Combining Convolutions with Attention Mechanisms
Automatic speech recognition (ASR) research generally focuses on high-resource languages such as English, which is supported by hundreds of thousands of hours...
Changing CTC Rules to Reduce Memory Consumption in Training and Decoding
Loss functions for training automatic speech recognition (ASR) models are not set in stone. The older rules of loss functions are not necessarily optimal....
