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Showing papers from Indian Institute of Technology Delhi Show all papers

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Statistical Mixing Guarantees for Contractive Echo State Networks

Pradeep Singh, Balasubramanian Raman

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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45%Niche pick
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Dimension Bounds for Contractive Reservoir Computing from Input Entropy

Pradeep Singh, Kishore Babu Nampalle, Balasubramanian Raman

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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57%Worth a look
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ManifoldCache: Training-Free Diffusion Acceleration via Constraint Manifold Caching

Prashant Pandey, Sri Venkatraya Chowdary Devineni, Brejesh Lall

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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medium 0/10
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57%Worth a look
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AQBENCH: Benchmarking Neural Surrogates for Air Quality Forecasting

Siddharthan Dileep, Sanchit Bedi, Pareshbhai D Parmar, Ayush Maheshwari and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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medium 0/10
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57%Worth a look
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ProbMedTOD: A Bayesian Network Guided Task-Oriented Dialogue System for Patient History Taking

Vishal Vivek Saley, Bhavesh Gurnani, Dinesh Raghu, Mausam

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
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76%Highly rated
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Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

Agentic AI scientists serve as co-scientists but lack autonomous discovery due to flawed problem selection, missing tacit lab knowledge, compressed diversity, and inadequate benchmarks.

Harshit Bisht, Vinay Kumar, Kevin Maik Jablonka, Mausam and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 1/5
92%Must read
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CausalDriveBench: Evaluating Causal Reasoning in Vision-Language-Action Models for Autonomous Driving

CausalDriveBench evaluates causal reasoning in autonomous driving vision-language-action models via structured QA and counterfactual trajectories, finding weak causal understanding despite fluent reasoning and accurate baseline predictions.

Narendiran Chembu, Navvrat Rao, Shreedhar Kodate, Gayatri S Banda and 9 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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19/20 AI panelreviewers recommend it

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AI panel: 19 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 5/5
78%Highly rated
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems

GraMO couples graph interactions with temporal state updates in a single linear recurrence for latent interacting particle simulation, achieving lowest long-horizon prediction errors across benchmarks.

Karn Tiwari, Niladri Dutta, Prathosh AP, N M Anoop Krishnan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
74%Highly rated
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Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise

Exploiting data symmetries improves k-NN accuracy for selecting low-noise training subsets, yielding near-optimal performance despite high-dimensional label noise.

Kumar Shubham, Pavan Karjol, Kiran M K, Prathosh AP

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5