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ORCID3rd AIMLSystems 2024: Baton Rouge, LA, USA
- Proceedings of the 4th International Conference on AI-ML Systems, AIMLSystems 2024, Baton Rouge, Louisiana, USA, October 8-11, 2024. ACM 2024, ISBN 979-8-4007-1161-9
Research Track
- Andrea Cossu, Andrea Ceni, Davide Bacciu, Claudio Gallicchio:
Sparse Reservoir Topologies for Physical Implementations of Random Oscillators Networks. 1:1-1:9 - Massimo Pavan, Gioele Mombelli, Francesco Sinacori, Manuel Roveri:
TinySV: Speaker Verification in TinyML with On-device Learning. 2:1-2:10 - Shubham Gandhi, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff:
BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks. 3:1-3:9 - Pedro Pongelupe Lopes, Gerlando Gramaglia, Davide Bacciu, Humberto T. Marques-Neto:
Towards Forecasting Bus Arrival Thorough A Model Based On GNN+LSTM Using GTFS and Real-time Data. 4:1-4:9 - Renju C. Nair, Ashish Gatreddi, Madhav Rao, Muralidhara V. N:
Visual Perception Transformer: Robust image understanding on unseen transformations across wide-ranging dataset sizes. 5:1-5:9 - Francesco Puoti, Fabrizio Pittorino, Manuel Roveri:
Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting. 6:1-6:8 - Raveendra R. Hegde, Saurabh Sharma:
Self Supervised LLM Customizer(SSLC): Customizing LLMs on Unlabeled Data to Enhance Contextual Question Answering. 7:1-7:11 - Richa Verma, Srikar Babu Gadipudi, Srinarayana Nagarathinam, Harshad Khadilkar:
ORCHID: Offline RL for Control of HVAC in Buildings using Historical and Low-Fidelity Simulation Data. 8:1-8:9 - Luca Colombo:
Federated On-Device Learning of Integer-Based Convolutional Neural Networks. 9:1-9:9 - Bidyut Saha, Riya Samanta, Soumya Kanti Ghosh, Ram Babu Roy:
TinyML-Powered Gesture Wizardry: Low-Cost, Low-Power Two-Stage CNN for Static Hand Gesture Classification on MCU in Appliance Control. 10:1-10:9 - Mohd Manzar Abbas, Amit Ranjan, Aixin Hou, Supratik Mukhopadhyay:
Trans-ARG: Predicting Antibiotic Resistance Genes with a Transformer-Based Model and Pretrained Protein Language Model. 11:1-11:8 - Amit Ranjan, Adam Bess, Md Saiful Islam Sajol, Magesh Rajasekaran, Chris Alvin, Supratik Mukhopadhyay:
KG-DTA: A knowledge graph-based meta-path learning framework to predict drug-target binding affinity. 12:1-12:9 - Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup, David Dobson, Kendall N. Niles, Ken Pathak, Steven Sloan:
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements. 13:1-13:10 - Jiarui Li, Samuel J. Landry, Ramgopal R. Mettu:
GPU Acceleration for Markov Chain Monte Carlo Sampling. 14:1-14:8 - Rajat Singh, Raajita Bhamidipaty, Anjali Sharma, Srikanta Bedathur:
Tab2Graph: Transforming Heterogeneous Tables as Graphs. 15:1-15:9 - Abhijit Manatkar, Devarsh Patel, Hima Patel, Naresh Manwani:
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis. 16:1-16:11 - Nikita Karthikeyan, Hayagreev Jeyandran, Rohit Verma, Rajeev Shorey:
Selective Graph Convolutional Network for Efficient Routing. 17:1-17:9 - Alessandro Falcetta, Giulio Cristofaro, Lorenzo Epifani, Manuel Roveri:
VETT: VectorDB-Enabled Transfer-Learning for Time-Series Forecasting. 18:1-18:9
Industry Track
- Henry Liang, Yu Zhou, Vijay K. Gurbani:
Efficient and verifiable responses using Retrieval Augmented Generation (RAG). 19:1-19:6 - Aayush Chaudhary:
Assessing the Impact of Upselling in online fantasy gaming. 20:1-20:5 - Shubham Jain, Amit Gupta, Kumari Neha:
AI Enhanced Ticket Management System for optimized Support. 21:1-21:7 - Fengchen Liu, Jordan Jung, Wei Feinstein, Jeff D'Ambrogia, Gary Jung:
Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models. 22:1-22:7 - Giridhar Mandyam:
Remote Attestation and Secure AI in Systems-on-Chip/Systems-in-Package. 23:1-23:6 - Quazi Mishkatul Alam, Vinay Kolar, Marina Thottan:
Towards AI/ML-Driven Network Traffic Engineering. 24:1-24:8
Demo Track
- Bibek Paul, Archisman Bhowmick, Mayank Mishra, Sarthak Gupta, Rekha Singhal:
TASCA++ : A multi-agentic tool to scalably accelerate ML pipelines. 25:1-25:3 - Riya Samanta, Bidyut Saha, Soumya Kanti Ghosh:
LeafSense: A Portable, Low-Cost, Low-Power Plant Disease Diagnostic Device Using TinyML. 26:1-26:3
Workshop Track
- Isha Shamim, Rekha Singhal:
Methodology for Quality Assurance Testing of LLM-based Multi-Agent Systems. 27:1-27:5 - Yunsung Chung, Janet Wang, Jihun Hamm:
Bridging the Gap: Synthetic Data Augmentation through Inversion and Distribution Matching for Few-shot Learning. 28:1-28:5 - Harshit Verma, M. Bhargav, Ritvik, Chetana Gavankar, Prajna Devi Upadhyay:
Question-Answering System in Computer Science. 29:1-29:4 - Emmett Chen, Pallavi Bajpai, Smriti H. Bhandari:
Star and Constellation Recognition using YOLO framework. 30:1-30:6
Tutorials
- Manish Gupta:
Deep Learning Methods for Query Auto Completion. 31:1-31:2
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