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Found 30 papers, 13 papers with code
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning.
Identity Lock: Locking API Fine-tuned LLMs With Identity-based Wake Words
To implement this, we propose a fine-tuning method named IdentityLock that integrates the wake words at the beginning of a large proportion (90%) of the training text prompts, while modifying the responses of the remaining 10% to indicate refusals.
Safety at Scale: A Comprehensive Survey of Large Model Safety
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI).
Cosmos World Foundation Model Platform for Physical AI
In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups.
Citations and Trust in LLM Generated Responses
Question answering systems are rapidly advancing, but their opaque nature may impact user trust.
Edify 3D: Scalable High-Quality 3D Asset Generation
We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation.
SimulTron: On-Device Simultaneous Speech to Speech Translation
Simultaneous speech-to-speech translation (S2ST) holds the promise of breaking down communication barriers and enabling fluid conversations across languages.
Simultaneous Speech-to-Speech Translation
Speech-to-Speech Translation
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Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction
Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
ChatEL: Entity Linking with Chatbots
Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well.
EntGPT: Linking Generative Large Language Models with Knowledge Bases
Additionally, our instruction tuning method (EntGPT-I) improves micro-F_1 scores by 2. 1% on average in supervised EL tasks and outperforms several baseline models in six Question Answering tasks.
Multi-modal Domain Adaptation for REG via Relation Transfer
Domain adaptation, which aims to transfer knowledge between domains, has been well studied in many areas such as image classification and object detection.
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved.
Translatotron 3: Speech to Speech Translation with Monolingual Data
This paper presents Translatotron 3, a novel approach to unsupervised direct speech-to-speech translation from monolingual speech-text datasets by combining masked autoencoder, unsupervised embedding mapping, and back-translation.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model.
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research.
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
We demonstrate that ACProp has a convergence rate of $O(\frac{1}{\sqrt{T}})$ for the stochastic non-convex case, which matches the oracle rate and outperforms the $O(\frac{logT}{\sqrt{T}})$ rate of RMSProp and Adam.
Posthoc Verification and the Fallibility of the Ground Truth
Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches.
Reddit Entity Linking Dataset
We introduce and make publicly available an entity linking dataset from Reddit that contains 17, 316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement.
AdaBelief Optimizer: Adapting Stepsizes by theBelief in Observed Gradients
We propose AdaBelief optimizer to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability.
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step.
HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure.
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability.
