More Like this

1. Memory-Aware Active Learning in Mobile Sensing Systems

   https://doi.org/10.1109/TMC.2020.3003936  

   Esna Ashari, Zhila; Chaytor, Naomi S; Cook, Diane J; Ghasemzadeh, Hassan (June 2020, IEEE Transactions on Mobile Computing)

   null (Ed.)

   We propose a novel active learning framework for activity recognition, which takes limitations of the oracle into account when selecting wearable sensor data for annotation. It is inspired by human-beings' limited capacity to respond to prompts on their mobile device, introducing the notion of mindful active learning and proposing a computational framework, called EMMA, to maximize the active learning performance taking informativeness of sensor data, query budget, and human memory into account. We formulate this optimization problem, propose an approach to model memory retention, discuss the complexity of the problem, and propose a greedy heuristic to solve it. Additionally, we design an approach to perform mindful active learning in batch where multiple sensor observations are selected simultaneously for querying and design two instantiations of EMMA to perform active learning in batch mode. We demonstrate the effectiveness of our approach using three datasets and by simulating oracles with various memory strengths. Our results indicate that EMMA achieves an accuracy of, on average, 13.5% higher than the case when only informativeness of samples is considered. We observe that mindful active learning is most beneficial when the query budget is small and/or the oracle's memory is weak. 

   more » « less
2. Rule-Enhanced Active Learning for Semi-Automated Weak Supervision

   https://doi.org/10.3390/ai3010013  

   Kartchner, David; Nakajima An, Davi; Ren, Wendi; Zhang, Chao; Mitchell, Cassie S. (March 2022, AI)

   A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances. REGAL interactively creates high-quality labeling patterns from raw text, enabling a single annotator to accurately label an entire dataset after initialization with three keywords for each class. Experiments demonstrate that REGAL extracts up to 3 times as many high-accuracy labeling functions from text as current state-of-the-art methods for interactive weak supervision, enabling REGAL to dramatically reduce the annotation burden of writing labeling functions for weak supervision. Statistical analysis reveals REGAL performs equal or significantly better than interactive weak supervision for five of six commonly used natural language processing (NLP) baseline datasets. 

   more » « less
3. Memory enhancements from active control of learning emerge across development

   https://doi.org/10.1016/j.cognition.2019.01.010  

   Ruggeri, Azzurra; Markant, Douglas B.; Gureckis, Todd M.; Bretzke, Maria; Xu, Fei (May 2019, Cognition)
4. Unlocking enhanced thermal conductivity in polymer blends through active learning

   https://doi.org/10.1038/s41524-024-01261-2  

   Xu, Jiaxin; Luo, Tengfei (April 2024, npj Computational Materials)

   Abstract Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML, is employed to explore the TC of approximately 550,000 unlabeled polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration (R_g), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC andR_gimprovement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and an odds ratio calculation, emphasizing the impact of increasingR_gand H-bond interactions on enhancing polymer blend TC. 

   more » « less
5. Enhanced Diffusion of Catalytically Active Enzymes

   https://doi.org/10.1021/acscentsci.9b00228  

   Zhang, Yifei; Hess, Henry (May 2019, ACS Central Science)