Microsoft’s VALL-E 2: First Time Human Parity in Zero-Shot Text-to ...
WEBJun 11, 2024 · Over the past decade, significant breakthroughs in speech synthesis have emerged, driven by the development of neural networks and end-to-end modeling. Last year, Microsoft introduced VALL-E, a neural codec language model capable of synthesizing high-quality personalized speech from just a 3-second recording of an unseen speaker. This model notably outperformed the state-of-the-art zero-shot ...
Superior Alternatives to MLPs? Kolmogorov-Arnold Networks …
WEBMay 6, 2024 · Multi-layer perceptrons (MLPs) stand as the bedrock of contemporary deep learning architectures, serving as indispensable components in various machine learning applications. Leveraging the expressive power conferred by the universal approximation theorem, MLPs excel in approximating nonlinear functions, embodying a default choice for many tasks. However, despite their widespread adoption ...
NYU & Stanford’s GPUDrive: Achieving Over 1 Million Steps per …
WEBAug 30, 2024 · Multi-agent learning algorithms have excelled at producing superhuman planning abilities in various games, but their impact on real-world multi-agent planning systems remains limited. A major barrier is the massive data requirements—often billions of experience steps—needed for effective training. In a new paper GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS, a ...
Yann LeCun Cake Analogy 2.0 | Synced
WEBFeb 22, 2019 · Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).”The quip rippled across the AI community and confirmed LeCun a strong advocate of unsupervised learning, a machine learning ...
NVIDIA’s NV-Embed: Superior Performance in Embedding Tasks …
WEBMay 29, 2024 · For years, embedding models based on bidirectional language models have led the field, excelling in retrieval and general-purpose embedding tasks. However, past top-tier methods have relied on fine-tuning Large Language Models (LLMs) with extensive amounts of proprietary synthetic data from GPT-4, which isn't accessible to the broader community. In a new paper NV-Embed: Improved Techniques
Generalizable Audio AI: Discover the Power of SpeechVerse by …
WEBMay 21, 2024 · In a new paper SpeechVerse: A Large-scale Generalizable Audio Language Model, a research team from Amazon AWS AI Labs introduces SpeechVerse, a robust multi-task framework that leverages supervised instruction fine-tuning to achieve strong performance across various speech tasks. The team …
China’s GPT-3? BAAI Introduces Superscale Intelligence Model …
WEBMar 23, 2021 · The BAAI research team summarizes some of Wu Dao 1.0’s key contributions: Wu Dao – Wen Yuan introduces the open-source Chinese pretraining model (CPM). Based on CPM, the CPM-Distill model reduces language confusion by 38 percent and achieves better results on downstream tasks. Wu Dao – Wen Lan is the first Chinese generic multimodal ...
A Look at Recent Advances in Google Translate | Synced
WEBJun 10, 2020 · Incorporating these new advances, Google’s latest model scores an average five-point increase on BLEU compared to the previous GNMT model — with the class of 50 low-resources languages achieving an average seven-point boost. The researchers note that there is still much room for improvement in the quality of automatic translation for ...
Samsung’s MobileQuant: Bringing High-Performance Language …
WEBSep 4, 2024 · The results show that MobileQuant can reduce both inference latency and energy usage by 20% to 50%, all while maintaining accuracy comparable to models utilizing 16-bit activations. In conclusion, MobileQuant represents a significant advancement in the development of energy- and compute-efficient quantized …
NVIDIA’s Nemotron-4 15B Dominates Multilingual Domain, …
WEBFeb 27, 2024 · In summary, Nemotron-4 15B demonstrates unparalleled multilingual performance among general-purpose language models at its scale, even outperforming specialized models in the multilingual domain. Its success underscores the potential for large language models to be pre-trained on extensive …