Search Results for author:
Found 13 papers, 11 papers with code
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters.
Ranked #14 on
Visual Question Answering (VQA)
on InfiMM-Eval
MT3: Multi-Task Multitrack Music Transcription
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding.
Ranked #2 on
Music Transcription
on Slakh2100
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-instrument Music Synthesis with Spectrogram Diffusion
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes.
LLark: A Multimodal Instruction-Following Language Model for Music
Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio.
The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
We call this system the Chamber Ensemble Generator (CEG), and use it to generate a large dataset of chorales from four different chamber ensembles (CocoChorales).
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
These descriptions enable 1) collecting human-verified reference outputs for each instance; and 2) automatic evaluation of candidate multimodal generations using a text-only LLM, aligning with human judgment.
Benchmarking Distribution Shift in Tabular Data with TableShift
However, high-quality benchmarks for distribution shift in tabular machine learning tasks are still lacking despite the widespread real-world use of tabular data and differences in the models used for tabular data in comparison to text and images.
Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit
Understanding the existence of patterns and trends in this data could be useful to a variety of stakeholders, particularly as Detroit emerges from Chapter 9 bankruptcy, but the patterns in such data are often complex and multivariate and the city lacks dedicated resources for detailed analysis of this data.
Computers and Society
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Researchers have proposed many methods for fair and robust machine learning, but comprehensive empirical evaluation of their subgroup robustness is lacking.
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
We also find that stacked ensembling provides no additional benefits to overall performance or fairness compared to either a local model or the zero-shot transfer procedure we tested.
Dropout Model Evaluation in MOOCs
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building.
MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data
MORF has the potential to accelerate and democratize research on its massive data repository, which currently includes over 200 MOOCs, as demonstrated by initial research conducted on the platform.
