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University of Cambridge > Talks.cam > Artificial Intelligence Research Group Talks (Computer Laboratory)
Artificial Intelligence Research Group Talks (Computer Laboratory)
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Tuesday lunch seminar featuring research talks from the AI group members and guest talks. If you have a question about this list, please contact: Mateja Jamnik; Pietro Lio; Mateo Espinosa Zarlenga. If you have a question about a specific talk, click on that talk to find its organiser. 1 upcoming talk and 124 talks in the archive. TacticAI: an AI assistant for football tactics
POSTPONEDPOSTPONED. Sorry for inconvenience.
Learning to Receive Help: Intervention-Aware Concept Embedding Models
Towards Learning-Powered Networked Systems
Text-and-audio methods
Accelerating Generative AI on Custom Hardware Accelerators - Challenges and Opportunities
Vibe checks and red teaming: why ML researchers are increasingly reverting to manual evaluation
Deep screening of RNA, XNA and protein interactionshttps://cl-cam-ac-uk.zoom.us/j/83721191406?pwd=V0xkY0Vvb3czWUJkR0xIZC9qMnM0QT09
Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data
RetroBridge: Modeling Retrosynthesis with Markov BridgesThis talk is online only: https://cam-ac-uk.zoom.us/j/92041617729
Multi-Agent Graph Learning-Based Optimization and its Applications to Computer Networks
Multi-Agent Simulation and Learning in TorchRL
Revolutionizing Biosensing Technologies: Plug and Play Devices in Medical and Agricultural Diagnostics
AbDiffuser: Full-Atom Generation of In Vitro Functioning AntibodiesNotice that this talk is online only: https://cam-ac-uk.zoom.us/j/92041617729 Pietro Lio and Chaitanya Joshi will introduce the speakers
On Learning Latent Models with Multi-Instance Weak Supervision
Sediment core analysis using artificial intelligence
Scattering Networks and Singular Values Decomposition: different methods to remove background in Single-Molecule Localization Microscopy imagehybrid (zoom link at the end of the abstract)
Sphere Neural-Network for Higher-level Cognitionjoin us also with zoom
Is ChatGPT adequate for decision-making?
Protein generation and fitness optimizationhttps://cl-cam-ac-uk.zoom.us/j/94094286505?pwd=T2V3MFVSQ1ZKTFFyYlFueHlzTE83Zz09
Interpretable Neural-Symbolic Concept ReasoningNote this is on zoom only! Note a change of date! Note a new zoom link!
POSTPONED: Interpretable Neural-Symbolic Concept ReasoningThis talk has moved to 20 June 2023.
Untangling genome assembly graphs with graph neural networkshttps://cl-cam-ac-uk.zoom.us/j/93934749405?pwd=M0ZERUZleklON01vZlpXbzhqTld0Zz09. ; ID riunione: 939 3474 9405 Passcode: 374750
Information Spreading on NetworksNOTE A CHANGE OF ROOM DUE TO EXAMS. NOW SS03
Hypergraph Factorisation for Multi-Tissue Gene Expression ImputationNOTE: Room changed, now Lecture Theatre 1
MaRF: Representing Mars as Neural Radiance Fields
Unveiling Bounded Confidence Dynamics in Sheaf Neural Networks
Improving learning with noisy labels in two possibile scenarios.
Relative Entropy Coding for Learned Data Compression
Integrating Radiomics and Explainable Methods: Paving the Way for Transparent and Interpretable Medical Imaging Analysis
Dimensionality Reduction via Probabilistic Inference
Structure-based Drug Design and Molecular Optimisation with Diffusion Models
Neural Networks for High-Dimensional Tabular Biomedical Datasets
Relative representations enable zero-shot latent space communicationOrganized by Donato Crisostomi and Pietro Barbiero
Composite Feature Selection using Deep Ensembles
When Vision Transformers Meet Cooperative Perception
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-off
A Distance Function based Cascaded Neural Network for accurate Polyps Segmentation and Classificationhttps://zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
Approximate Equivariance SO(3) Needlet Convolutionhttps://zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
Global Explainability of GNNs via Logic Combination of Learned Conceptshttps://zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
The Interpretability of Graph Neural NetworksJoin Zoom Meeting https://zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
Learning Feynman Diagrams using Graph Neural Networkshttps://zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
Learning Multi-Scene Absolute Pose Regression with Transformers
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
Graph Neural Networks for Geometric Graphs
AI for Science: An Oral Report from a Recent Dagstuhl Workshop
ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networkshttps://cl-cam-ac-uk.zoom.us/j/99166955895?pwd=SzI0M3pMVEkvNmw3Q0dqNDVRalZvdz09
Query-based Hard-Image Retrieval for Object Detection at Test Time
Cancelled! Sorry for inconvenience.
Learning Backward Compatible Embeddingshybrid event (https://cl-cam-ac-uk.zoom.us/j/92564083880?pwd=Wk5DVWpUc2lIN0krMFU5azEwUGpEUT09)
The Problem of Size Generalization in Graph Neural NetworksRecording: https://www.youtube.com/watch?v=W4jNbgtBl_k
Neighbourhood matching creates realistic surrogate temporal networks
Neural Sequence Models for Mathematical ReasoningRESCHEDULED, NOTE THE UNUSUAL TIME
Multi-fidelity machine learning models for improved high-throughput screening predictions
Lifted Relational Neural Networks
On the Two-fold Role of Logic Constraints in Deep Learninghttps://cl-cam-ac-uk.zoom.us/j/99805544705?pwd=cXR6MTlaeXd6VmEreVdQSmFRblBtUT09
Machine learning as an assay for high-dimensional biology
Work in progress: Making efficient use of language models for theorem proving
POSTPONED till next week!!!! Work in progress: Making efficient use of language models for theorem provingPostponed till next week
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Brain charts for the human lifespan
Advancing mathematics by guiding human intuition with AI
An Introduction to Federated Learning and its Applications in Medicine
Learning on Graphs with Missing Node Features
Weisfeiler and Lehman Go Cellular: CW Networks
Gaussian Processes on Graphs via Spectral Kernel Learning
3D Pre-training improves GNNs for Molecular Property Prediction
Unlocking Deep Learning for Graphs
Structure-aware generation of molecules in protein pockets
Geometric Deep Learning: Grids, Graphs, Groups, Geodesics, and Gauges
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Towards Robust and Reliable Model Explanations
Predicting outcome for psychotic disorders, using brain connectivity and transcribed speech data
Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning
Computational foundations of morphogenomics
Landscape and Gaps in Open Source Fairness Toolkits
Neural ODE Processes
Learning under model misspecification
Latent Action Space for Offline Reinforcement Learning
Predicting material properties with the help of machine learning
Improving the Aggregation in Graph Networks: can nodes understand their neighbourhood?
Wasserstein Natural Gradients for Reinforcement Learning
Graph Neural Networks for Biomedical DataNote the change of date and time. This seminar is part of the Wednesday seminar series too.
What is Next for the Efficient Machine Learning Revolution?
Second Order Behaviour in Augmented Neural ODEs
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Predicting Mortality and Length of Stay with Patient Graph Representation Learning
Sponge Examples: Energy-Latency Attacks on Neural Networks
Emergent Multi-Agent Communication: The story so far
A Weighting Based Adversarial Approach to Fairness in Machine Learning
Adversarial Explanations - You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods
Developing a Concept-Oriented Search Engine for Isabelle Based on Natural Language : Technical Challenges
Graph Representation Learning under Uncertainty (WIP)
Temporal Pointwise Convolutional Networks for Length of Stay Prediction in the Intensive Care Unit
Learning evolving node and community representations on dynamic graphs
Deep Graph Mapper: Seeing Graphs through the Neural Lens
Knowledge Graph Convolutional Networks - Combining ML and Reasoning
Some ideas on neural network modulation
KRAB zinc fingers, transposable elements and the evolution of gene regulatory networks
Machine learning for network inference
Non-convex Optimisation Using the Polyak-Łojasiewicz Inequality
Flexible deep learning for heterogeneous clinical time series
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
A Geometrical Perspective on Deep Neural Networks
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
Work in progress: Diving deeper into building distributed representations graphs
AI, Data Science & Mechanism Design in Sub-Saharan Africa
Model extraction for clinical decision support systems
Finding an Analogy
Matrix Means for Signed and Multilayer Graph Clustering
Rule extraction from deep neural nets
Machine Learning Systems Design
Question Answering in Realistic Visual Environments: Challenges and Approaches
Neural networks to model probability densities in quantum chromodynamics
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
Verifiable robustness of neural networks in autonomous vehicles.
A human-oriented term rewriting system
Dilated DenseNets for Relational Reasoning
Quick intro to building and analysing distributed representation of graphs
Please see above for contact details for this list. |
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Zhe Wang (DeepMind).
Tuesday 12 March 2024, 13:00-14:00