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Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation
23 Aug

Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation

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arXiv:2408.11849v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has significantly propelled the development of text-based chatbots, demonstrating their capability to engage in coherent and contextually relevant dialogues. However, extending these advancements to enable end-to-end speech-to-speech conversation bots remains a formidable challenge, primarily due to the extensive dataset and computational resources required. The conventional approach of cascading automatic speech recognition (ASR), LLM, and text-to-speech (TTS) models in a pipeline, while effective, suffers from unnatural prosody because it lacks direct interactions between the input audio and its transcribed text and the output audio. These systems are also…
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Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?
23 Aug

Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?

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[Submitted on 21 Aug 2024] View a PDF of the paper titled Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?, by Francesco Innocenti and 3 other authors View PDF HTML (experimental) Abstract:Predictive coding (PC) is an energy-based learning algorithm that performs iterative inference over network activities before weight updates. Recent work suggests that PC can converge in fewer learning steps than backpropagation thanks to its inference procedure. However, these advantages are not always observed, and the impact of PC inference on learning is theoretically not well understood. Here, we study the geometry of the PC energy landscape…
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Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs
23 Aug

Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs

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arXiv:2408.11207v1 Announce Type: new Abstract: The field of autonomous vehicles (AVs) predominantly leverages multi-modal integration of LiDAR and camera data to achieve better performance compared to using a single modality. However, the fusion process encounters challenges in detecting distant objects due to the disparity between the high resolution of cameras and the sparse data from LiDAR. Insufficient integration of global perspectives with local-level details results in sub-optimal fusion performance.To address this issue, we have developed an innovative two-stage fusion process called Quantum Inverse Contextual Vision Transformers (Q-ICVT). This approach leverages adiabatic computing in quantum concepts to create a novel reversible…
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MGH Radiology Llama: A Llama 3 70B Model for Radiology
23 Aug

MGH Radiology Llama: A Llama 3 70B Model for Radiology

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arXiv:2408.11848v1 Announce Type: new Abstract: In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (LLMs) have emerged as particularly promising tools, offering significant potential in assisting radiologists with report generation, clinical decision support, and patient communication. This paper presents an advanced radiology-focused large language model: MGH Radiology Llama. It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2. Leveraging a unique and comprehensive dataset from Massachusetts General Hospital, comprising over 6.5 million…
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What Is Data Quality Framework? Components & Implementation
23 Aug

What Is Data Quality Framework? Components & Implementation

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Image generated with MidjourneyOrganizations increasingly rely on data to make business decisions, develop strategies, or even make data or machine learning models their key product. As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of  a data quality framework, its essential components, and how to implement it effectively within your organization.What is a data quality framework?A data quality framework refers to the principles, processes, and standards designed to manage and improve the quality of data within organizations. Such frameworks ensure that the data used for…
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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
23 Aug

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

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arXiv:2408.11974v1 Announce Type: new Abstract: We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving structured nonconvex minimax optimization problems in the form of $min_textbf{x} max_{textbf{y} in Y} f(textbf{x}, textbf{y})$, where the objective function $f(textbf{x}, textbf{y})$ is nonconvex in $textbf{x}$ and concave in $textbf{y}$, and the constraint set $Y subseteq mathbb{R}^n$ is convex and bounded. In the convex-concave setting, the single-timescale GDA achieves strong convergence guarantees and has been used for solving application problems arising from operations research and computer science. However, it can fail to converge in more general settings. Our contribution in this paper is…
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Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles
23 Aug

Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles

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arXiv:2408.11196v1 Announce Type: new Abstract: Advances in machine learning algorithms for sensor fusion have significantly improved the detection and prediction of other road users, thereby enhancing safety. However, even a small angular displacement in the sensor's placement can cause significant degradation in output, especially at long range. In this paper, we demonstrate a simple yet generic and efficient multi-task learning approach that not only detects misalignment between different sensor modalities but is also robust against them for long-range perception. Along with the amount of misalignment, our method also predicts calibrated uncertainty, which can be useful for filtering and fusing predicted…
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Prompto: An open source library for asynchronous querying of LLM endpoints
23 Aug

Prompto: An open source library for asynchronous querying of LLM endpoints

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arXiv:2408.11847v1 Announce Type: new Abstract: Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits.…
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Valuing an Engagement Surface using a Large Scale Dynamic Causal Model
23 Aug

Valuing an Engagement Surface using a Large Scale Dynamic Causal Model

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arXiv:2408.11967v1 Announce Type: new Abstract: With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by…
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Compress Guidance in Conditional Diffusion Sampling
23 Aug

Compress Guidance in Conditional Diffusion Sampling

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arXiv:2408.11194v1 Announce Type: new Abstract: Enforcing guidance throughout the entire sampling process often proves counterproductive due to the model-fitting issue., where samples are generated to match the classifier's parameters rather than generalizing the expected condition. This work identifies and quantifies the problem, demonstrating that reducing or excluding guidance at numerous timesteps can mitigate this issue. By distributing the guidance densely in the early stages of the process, we observe a significant improvement in image quality and diversity while also reducing the required guidance timesteps by nearly 40%. This approach addresses a major challenge in applying guidance effectively to generative tasks.…
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