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Showing papers from Indian Institute of Science, Indian institute of science, Bangalore Show all papers

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Lost in the Slots: Revisiting Object-Centric Representations in the era of Foundation Models

Priyam Dey, Aditya Sahdev, Omkar M Kashyap, Venkatesh Babu Radhakrishnan

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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45%Niche pick
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Adaptive multiscale operator correction via learned spectral subspace and physics-informed optimization.

Subham Patel, Himanshu Pandey, RATIKANTA BEHERA

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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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In STeP: Speculative Tensor Parallelism for Concurrent Heterogeneous Inference of LLMs

Viren Luke Radhakrishnan, Dhruva Kashyap, Pranav K Nayak, Chiranjib Bhattacharyya and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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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78%Highly rated
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VAANI: Capturing the language landscape for an inclusive digital India

Project VAANI releases a multimodal dataset of 31,255 speech hours and 289K images spanning 105 Indic languages across 165 Indian districts to support inclusive speech technology.

Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu, Nihar Desai and 16 more

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

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
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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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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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5
72%Highly rated
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Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates

FAR-SIGN enables adversary-resilient fully asynchronous parameter-server optimization via signed directional updates with two-timescale bias correction, achieving near-optimal convergence rates for nonconvex objectives.

Anik Kumar Paul, Nibedita Roy, Nagesh Talagani, Swetha Ganesh and 2 more

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

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 1/5
78%Highly rated
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Thinking in Boxes: 3D Editing in Real Images Made Easy

The method treats 3D box pairs as structured transformation specs for precise real-image editing, outperforming state-of-the-art on large 3D edits.

Pradhaan Bhat, Naveen Chandra R, Rishubh Parihar, Vaibhav Vavilala and 3 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5