28 citations · 52 across the 16 of their papers we have counts for
16 papers
Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs
Akhiad Bercovich, Talor Abramovich, Daniel Afrimi +67
We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maximize server throughput under…
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning
Dongki Jung, Jaehoon Choi, Adil Qureshi +3
We present Wid3R, a feed-forward neural network for multi-view visual geometry reconstruction that supports wide field-of-view camera models. Unlike existing methods that assume re…
NVIDIA Nemotron 3: Efficient and Open Intelligence
NVIDIA, :, Aaron Blakeman +356
We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a…
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +311
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 t…
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs
Akhiad Bercovich, Tomer Ronen, Talor Abramovich +23
Large language models (LLMs) offer remarkable capabilities, yet their high inference costs restrict wider adoption. While increasing parameter counts improves accuracy, it also bro…
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
Amnon Geifman, Daniel Barzilai, Ronen Basri +1
Wide neural networks are biased towards learning certain functions, influencing both the rate of convergence of gradient descent (GD) and the functions that are reachable with GD i…
A Kernel Perspective of Skip Connections in Convolutional Networks
Daniel Barzilai, Amnon Geifman, Meirav Galun +1
Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural architectures for image processing. Here we study their properties through their…
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
Amnon Geifman, Meirav Galun, David Jacobs +1
We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with no…
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks
Yuval Belfer, Amnon Geifman, Meirav Galun +1
Deep residual network architectures have been shown to achieve superior accuracy over classical feed-forward networks, yet their success is still not fully understood. Focusing on…
On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman, Abhay Yadav, Yoni Kasten +3
Recent theoretical work has shown that massively overparameterized neural networks are equivalent to kernel regressors that use Neural Tangent Kernels(NTK). Experiments show that t…
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri, Meirav Galun, Amnon Geifman +3
Recent works have partly attributed the generalization ability of over-parameterized neural networks to frequency bias -- networks trained with gradient descent on data drawn from…
Averaging Essential and Fundamental Matrices in Collinear Camera Settings
Amnon Geifman, Yoni Kasten, Meirav Galun +1
Global methods to Structure from Motion have gained popularity in recent years. A significant drawback of global methods is their sensitivity to collinear camera settings. In this…
Algebraic Characterization of Essential Matrices and Their Averaging in Multiview Settings
Yoni Kasten, Amnon Geifman, Meirav Galun +1
Essential matrix averaging, i.e., the task of recovering camera locations and orientations in calibrated, multiview settings, is a first step in global approaches to Euclidean stru…
GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices
Yoni Kasten, Amnon Geifman, Meirav Galun +1
This paper addresses the problem of recovering projective camera matrices from collections of fundamental matrices in multiview settings. We make two main contributions. First, giv…