Zobrazeno 1 - 10
of 4 546
pro vyhledávání: '"Sung JU"'
Songwriting is often driven by multimodal inspirations, such as imagery, narratives, or existing music, yet songwriters remain unsupported by current music AI systems in incorporating these multimodal inputs into their creative processes. We introduc
Externí odkaz:
http://arxiv.org/abs/2412.18940
Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potentia
Externí odkaz:
http://arxiv.org/abs/2412.18232
Eating disorders (ED) are complex mental health conditions that require long-term management and support. Recent advancements in large language model (LLM)-based chatbots offer the potential to assist individuals in receiving immediate support. Yet,
Externí odkaz:
http://arxiv.org/abs/2412.11656
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training da
Externí odkaz:
http://arxiv.org/abs/2412.02186
As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has become essential. Fine-tuning with paraphrased data is a common approach to
Externí odkaz:
http://arxiv.org/abs/2411.00686
Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case
Externí odkaz:
http://arxiv.org/abs/2410.23227
Autor:
Choi, Ryuhaerang, Chatterjee, Soumyajit, Spathis, Dimitris, Lee, Sung-Ju, Kawsar, Fahim, Malekzadeh, Mohammad
Developing new machine learning applications often requires the collection of new datasets. However, existing datasets may already contain relevant information to train models for new purposes. We propose SoundCollage: a framework to discover new cla
Externí odkaz:
http://arxiv.org/abs/2410.23008
Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. Due to these capabilities, many recent methods are proposed to automatically refine the codes with LLMs. However, we should rethink that the
Externí odkaz:
http://arxiv.org/abs/2410.22375
Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools,
Externí odkaz:
http://arxiv.org/abs/2410.02958
Information Retrieval (IR) methods aim to identify documents relevant to a query, which have been widely applied in various natural language tasks. However, existing approaches typically consider only the textual content within documents, overlooking
Externí odkaz:
http://arxiv.org/abs/2410.02729