Model Fusion via Optimal Transport

Combining different models is a widely used paradigm in machine learning applications. While the most common approach is to form an ensemble of models and average their individual predictions, this approach is often rendered infeasible by given resource constraints in terms of memory and computation, which grow linearly with the number of models. We present a layer-wise model fusion algorithm for neural networks that utilizes optimal transport to (soft-) align neurons across the models before averaging their associated parameters. We show that this can successfully yield "one-shot" knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d. data. In both i.i.d. and non-i.i.d. settings, we illustrate that our approach significantly outperforms vanilla averaging, as well as how it can serve as an efficient replacement for the ensemble with moderate fine-tuning, for standard convolutional networks (like VGG11), residual networks (like ResNet18), and multi-layer perceptrons on CIFAR10, CIFAR100, and MNIST. Finally, our approach also provides a principled way to combine the parameters of neural networks with different widths, and we explore its application for model compression.

Deep(ly) Learning the Depth

Xun Victor Suo

We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a regression problem. Such a method can replace camera calibration since no knowledge on the camera configuration is required during training. Another advantage of deep learning for this problem, is the speed of testing; typically a few microseconds per point in the scene. This is a lot better than state-of-art algorithms that require to solve a full optimization problem. In a second part, we have studied a related problem: detecting changes in the camera setting. We have shown that deep learning classifiers can recognize amongst a few (4 or 5) camera settings based only on the projections of points on the cameras, with less than 1% classification error. This is a promising step towards the SLAM problem.

2016

Hardware-Friendly Learning Algorithms for Neural Networks: An Overview

The hardware implementation of artificial neural networks and their learning algorithms is a fascinating area of research with far-reaching applications. However, the mapping from an ideal mathematical model to compact and reliable hardware is far from evident. This paper presents an overview of various methods that simplify the hardware implementation of neural network models. Adaptations that are proper to specific learning rules or network architectures are discussed. These range from the use of perturbation in multilayer feedforward networks and local learning algorithms to quantization effects in self-organizing feature maps. Moreover, in more general terms, the problems of inaccuracy, limited precision, and robustness are treated.

IEEE Computer Society Press1996

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning.However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep neural networks output incorrect results.Understanding adversarial attacks and designing algorithms to make deep neural networks robust against these attacks are key steps to building reliable artificial intelligence in real-life applications.In this thesis, we will first formulate the robust learning problem.Based on the notations of empirical robustness and verified robustness, we design new algorithms to achieve both of these types of robustness.Specifically, we investigate the robust learning problem from the optimization perspectives.Compared with classic empirical risk minimization, we show the slow convergence and large generalization gap in robust learning.Our theoretical and numerical analysis indicates that these challenges arise, respectively, from non-smooth loss landscapes and model's fitting hard adversarial instances.Our insights shed some light on designing algorithms for mitigating these challenges.Robust learning has other challenges, such as large model capacity requirements and high computational complexity.To solve the model capacity issue, we combine robust learning with model compression.We design an algorithm to obtain sparse and binary neural networks and make it robust.To decrease the computational complexity, we accelerate the existing adversarial training algorithm and preserve its performance stability.In addition to making models robust, our research provides other benefits.Our methods demonstrate that robust models, compared with non-robust ones, usually utilize input features in a way more similar to the way human beings use them, hence the robust models are more interpretable.To obtain verified robustness, our methods indicate the geometric similarity of the decision boundaries near data points.Our approaches towards reliable artificial intelligence can not only render deep neural networks more robust in safety-critical applications but also make us better aware of how they work.

EPFL2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

EPFL2022