Machine Learning in Signal Processing

Generative Models

Learning to generate realistic samples using models like Flow matching, Diffusion models, GANs and VAEs.

Key Focus Areas:

• Novel Architecture Design: Developing new generative model architectures that improve sample quality, training stability, and computational efficiency.
• Controllable Generation: Investigating methods for disentangling latent factors to allow for fine-grained control over generated outputs.
• Data Augmentation and Synthesis: Utilizing generative models to create synthetic data for training robust machine learning models, especially in data-scarce scenarios.
• Conditional Generation: Exploring techniques for generating data conditioned on specific inputs or properties, relevant for tasks like image-to-image translation or text-to-image synthesis.

Relevant Publications:

Anomaly Detection

Identifying rare or unusual patterns in complex data.

Key Focus Areas:

• Unsupervised and Semi-Supervised Anomaly Detection: Developing algorithms that can learn normal behavior without explicit anomaly labels or with very few labels.
• Deep Anomaly Detection: Utilizing deep neural networks to learn powerful representations for distinguishing normal from anomalous data points.
• Streaming Anomaly Detection: Addressing the challenge of detecting anomalies in real-time data streams.
• Explainable Anomaly Detection: Providing insights into why a particular data point is flagged as anomalous.

Relevant Publications:

Uncertainty Estimation

Modeling confidence in predictions for robust ML applications.

Key Focus Areas:

• Bayesian Neural Networks: Exploring Bayesian approaches to deep learning for principled uncertainty quantification.
• Ensemble Methods for Uncertainty: Developing efficient ensemble techniques to capture model uncertainty without the full computational cost of traditional Bayesian methods.
• Uncertainty-Aware Training: Designing training procedures and loss functions that encourage models to output reliable uncertainty estimates.
• Calibration of Uncertainty: Ensuring that predicted uncertainties accurately reflect the true likelihood of errors.

Relevant Publications:

Out-of-Distribution Detection

Detecting data points not seen during training.

Key Focus Areas:

• Novel OOD Scoring Functions: Developing effective metrics and approaches to quantify how “out-of-distribution” a given input is.
• Training for OOD Robustness: Designing training strategies that explicitly teach models to distinguish between in-distribution and OOD samples.
• Connections to Anomaly and Novelty Detection: Exploring the relationships and distinctions between OOD detection and other forms of outlier analysis.
• Evaluation Benchmarks: Contributing to the development of rigorous benchmarks for assessing OOD detection performance across various domains.

Relevant Publications:

Few-Shot Learning

Learning from very limited labeled data.

Key Focus Areas:

• Meta-Learning for Few-Shot Tasks: Developing models that learn to learn, enabling them to quickly adapt to new tasks with minimal examples.
• Metric Learning: Designing embedding spaces where similar instances are close and dissimilar ones are far apart, facilitating comparison-based few-shot classification.
• Data Augmentation and Synthesis: Generating diverse synthetic data to augment limited real datasets for few-shot learning.
• Few-Shot Reinforcement Learning: Applying few-shot principles to enable agents to learn new skills with limited interactions.

Relevant Publications:

Hardware-Aware Machine Learning

Optimizing models for efficiency on various hardware platforms.

Key Focus Areas:

• Model Compression: Investigating techniques like network pruning, weight quantization, and knowledge distillation to reduce model size and inference latency.
• Neural Architecture Search (NAS): Automating the design of efficient neural network architectures tailored for specific hardware constraints and performance targets.
• Efficient Inference on Edge Devices: Developing methods to deploy high-performing machine learning models on low-power, resource-limited hardware.
• Energy-Efficient AI: Researching techniques that minimize the energy consumption of deep learning models during training and inference.

Relevant Publications:

Application Areas

Focusing on practical applications including Depth Estimation, 3D Point Cloud Processing, and other real-world problems.

Key Focus Areas:

• Robust Depth Estimation: Developing techniques for accurately inferring per-pixel distance from images or video, crucial for autonomous navigation and augmented reality.
• Advanced 3D Point Cloud Analysis: Focusing on processing sparse and dense 3D point clouds for tasks like semantic segmentation, object detection, and scene understanding in real-world environments.
• Real-time Inference for Perception: Optimizing models for efficient execution on embedded systems to enable real-time perception in applications like automated driving and robotics.
• Multi-modal Data Fusion: Integrating information from various sensors (e.g., cameras, LiDAR) to enhance the robustness and accuracy of perception systems.

Relevant Publications: