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NVIDIA Research at ICLR — the Next Wave of Multimodal Generative AI

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5 months ago

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Advancing AI requires a full-stack approach, with a powerful foundation of computing infrastructure — including accelerated processors and networking technologies — connected to optimized compilers, algorithms and applications.

NVIDIA Research is innovating across this spectrum, supporting virtually every industry in the process. At this week’s International Conference on Learning Representations (ICLR), taking place April 24-28 in Singapore, more than 70 NVIDIA-authored papers introduce AI developments with applications in autonomous vehicles, healthcare, multimodal content creation, robotics and more.

“ICLR is one of the world’s most impactful AI conferences, where researchers introduce important technical innovations that move every industry forward,” said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. “The research we’re contributing this year aims to accelerate every level of the computing stack to amplify the impact and utility of AI across industries.”

Research That Tackles Real-World Challenges

Several NVIDIA-authored papers at ICLR cover groundbreaking work in multimodal generative AI and novel methods for AI training and synthetic data generation, including: 

  • Fugatto: The world’s most flexible audio generative AI model, Fugatto generates or transforms any mix of music, voices and sounds described with prompts using any combination of text and audio files. Other NVIDIA models at ICLR improve audio large language models (LLMs) to better understand speech.
  • HAMSTER: This paper demonstrates that a hierarchical design for vision-language-action models can improve their ability to transfer knowledge from off-domain fine-tuning data — inexpensive data that doesn’t need to be collected on actual robot hardware — to improve a robot’s skills in testing scenarios.   
  • Hymba: This family of small language models uses a hybrid model architecture to create LLMs that blend the benefits of transformer models and state space models, enabling high-resolution recall, efficient context summarization and common-sense reasoning tasks. With its hybrid approach, Hymba improves throughput by 3x and reduces cache by almost 4x without sacrificing performance.
  • LongVILA: This training pipeline enables efficient visual language model training and inference for long video understanding. Training AI models on long videos is compute and memory-intensive — so this paper introduces a system that efficiently parallelizes long video training and inference, with training scalability up to 2 million tokens on 256 GPUs. LongVILA achieves state-of-the-art performance across nine popular video benchmarks.
  • LLaMaFlex: This paper introduces a new zero-shot generation technique to create a family of compressed LLMs based on one large model. The researchers found that LLaMaFlex can generate compressed models that are as accurate or better than state-of-the art pruned, flexible and trained-from-scratch models — a capability that could be applied to significantly reduce the cost of training model families compared to techniques like pruning and knowledge distillation.
  • Proteina: This model can generate diverse and designable protein backbones, the framework that holds a protein together. It uses a transformer model architecture with up to 5x as many parameters as previous models.
  • SRSA: This framework addresses the challenge of teaching robots new tasks using a preexisting skill library — so instead of learning from scratch, a robot can apply and adapt its existing skills to the new task. By developing a framework to predict which preexisting skill would be most relevant to a new task, the researchers were able to improve zero-shot success rates on unseen tasks by 19%.
  • STORM: This model can reconstruct dynamic outdoor scenes — like cars driving or trees swaying in the wind — with a precise 3D representation inferred from just a few snapshots. The model, which can reconstruct large-scale outdoor scenes in 200 milliseconds, has potential applications in autonomous vehicle development.

Discover the latest work from NVIDIA Research, a global team of around 400 experts in fields including computer architecture, generative AI, graphics, self-driving cars and robotics. 



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Databricks at a crossroads: Can its AI strategy prevail without Naveen Rao?

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3 hours ago

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September 13, 2025

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“Databricks is in a tricky spot with Naveen Rao stepping back. He was not just a figurehead, but deeply involved in shaping their AI vision, particularly after MosaicML,” said Robert Kramer, principal analyst at Moor Insights & Strategy.

“Rao’s absence may slow the pace of new innovation slightly, at least until leadership stabilizes. Internal teams can keep projects on track, but vision-driven leaps, like identifying the ‘next MosaicML’, may be harder without someone like Rao at the helm,” Kramer added.

Rao became a part of Databricks in 2023 after the data lakehouse provider acquired MosaicML, a company Rao co-founded, for $1.3 billion. During his tenure, Rao was instrumental in leading research for many Databricks products, including Dolly, DBRX, and Agent Bricks.



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NFL player props, odds: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

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4 hours ago

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September 13, 2025

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The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.

There’s a Monday Night Football doubleheader featuring star pass catchers like Nico Collins, Mike Evans and Brock Bowers. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

Now, with the Week 2 NFL schedule quickly approaching, SportsLine’s Machine Learning Model AI has identified the top NFL props from the biggest Week 2 games.

Week 2 NFL props for Sunday’s main slate

After analyzing the NFL props from Sunday’s main slate and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Lions receiver Amon-Ra St. Brown goes Over 63.5 receiving yards (-114) versus the Bears at 1 p.m. ET. Detroit will host this contest, which is notable as St. Brown has averaged 114 receiving yards over his last six home games. He had at least 70 receiving yards in both matchups versus the Bears a year ago.

Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.

Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football

After analyzing Falcons vs. Vikings props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Falcons running back Bijan Robinson goes Over 65.5 rushing yards (-114). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. The SportsLine Machine Learning Model projects Robinson to have 81.8 yards on average in a 4.5-star prop pick. See more NFL props for Vikings vs. Falcons here. 

You can make NFL prop bets on Robinson, Justin Jefferson and others with the Underdog Fantasy promo code CBSSPORTS2. Pick at Underdog Fantasy and get $50 in bonus funds after making a $5 wager:

Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football

After analyzing Texans vs. Buccaneers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Bucs quarterback Baker Mayfield goes Under 235.5 passing yards (-114). While Houston has questions regarding its offense, there’s little worry about the team’s pass defense. In 2024, Houston had the second-most interceptions, the fourth-most sacks and allowed the fourth-worst passer rating. Since the start of last year, and including the playoffs, the Texans have held opposing QBs under 235.5 yards in 13 of 20 games. The SportsLine Machine Learning Model forecasts Mayfield to finish with just 200.1 passing yards, making the Under a 4-star NFL prop. See more NFL props for Buccaneers vs. Texans here. 

You can also use the latest FanDuel promo code to get $300 in bonus bets instantly:

Week 2 NFL props for Chargers vs. Raiders on Monday Night Football

After analyzing Raiders vs. Chargers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Chargers quarterback Justin Herbert goes Under 254.5 passing yards (-114). The Raiders’ defense was underrated in preventing big passing plays a year ago as it ranked third in the NFL in average depth of target allowed. It forced QBs to dink and dunk their way down the field, which doesn’t lead to big passing yardages, and L.A. generally prefers to not throw the ball anyway. Just four teams attempted fewer passes last season than the Chargers, and with L.A. running for 156.5 yards versus Vegas last season, Herbert shouldn’t be overly active on Monday night. He’s forecasted to have 221.1 passing yards in a 4.5-star NFL prop bet. See more NFL props for Chargers vs. Raiders here. 

How to make Week 2 NFL prop picks

SportsLine’s Machine Learning Model has identified another star who sails past his total and has dozens of NFL props rated 4 stars or better. You need to see the Machine Learning Model analysis before making any Week 2 NFL prop bets.

Which NFL prop picks should you target for Week 2, and which quarterback has multiple 5-star rated picks? Visit SportsLine to see the latest NFL player props from SportsLine’s Machine Learning Model that uses cutting-edge artificial intelligence to make its projections.





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In the News: Thomas Feeney on AI in Higher Education – Newsroom

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6 hours ago

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September 13, 2025

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“I had an interesting experience over the summer teaching an AI ethics class. You know plagiarism would be an interesting question in an AI ethics class … They had permission to use AI for the first written assignment. And it was clear that many of them had just fed in the prompt, gotten back the paper and uploaded that. But rather than initiate a sort of disciplinary oppositional setting, I tried to show them, look, what you what you’ve produced is kind of generic … and this gave the students a chance to recognize that they weren’t there in their own work. This opened the floodgates,” Feeney said.

“I think the focus should be less on learning how to work with the interfaces we have right now and more on just graduate with a story about how you did something with AI that you couldn’t have done without it. And then, crucially, how you shared it with someone else,” he continued.



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