Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite efforts to build real-world simulators, leveraging generative models for virtual worlds, like financial markets, remains underexplored. In financial markets, generative models can simulate market effects…
About The Workshop: Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in…
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally…
Informal Knowledge Sharing (KS) is vital for enduser programmers to gain expertise. To better understand how personal (self-efficacy), social (reputational gains, trust between colleagues), and software-related (codification effort) variables influence spreadsheet KS intention, we conducted a multiple regressions analysis based on survey data from spreadsheet…
Pediatric patients frequently experience stress before surgery, impacting their overall well-being. Our study introduces a unique robotic ride-on car aimed at reducing preoperative stress in children aged 3-10. Drawing inspiration from assistive driving technologies, this car is equipped with various sensors and intervention mechanisms that…
Large language models (LLMs) augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LLMs, such as Retrieval-Augmented Generation (RAG) and fine-tuning, are gaining increasing attention and widespread application. Nonetheless, the effective deployment of data-augmented LLMs across…
This article overviews some of the key past developments in cloud datacenter power and energy management, where we are today, and what the future could be. This topic is gaining enormous, renewed interest in the context of the conflicting needs of the AI revolution and…
We introduce a multi-modal diffusion model tailored for the bi-directional conditional generation of video and audio. We propose a joint contrastive training loss to improve the synchronization between visual and auditory occurrences. We present experiments on two datasets to evaluate the efficacy of our proposed…
Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. Examples include transferring room acoustics or altering audio effects such as distortion. This paper investigates the potential of Gaussian Flow Bridges, an emerging…
Large, pretrained model architectures have demonstrated potential in a wide range of audio recognition and classification tasks. These architectures are increasingly being used in Speech Emotion Recognition (SER) as well, an area that continues to grapple with the scarcity of data, and especially of labeled…
We reflect on our experiences in improving the diversity of participants in our research, focusing on geographic diversity and countering WEIRDness. Our reflections are grounded in four studies conducted over two years, with more than 100 total participant engagements across more than 100 hours of…
We present a cost-effective method to integrate speech into a large language model (LLM), resulting in a Contextual Speech Model with Instruction-following/in-context-learning Capabilities (COSMIC) multi-modal LLM. Using GPT-3.5, we generate Speech Comprehension Test Question-Answer (SQA) pairs from speech transcriptions for supervised instruction tuning. With under…
