We consider the problem of online learning with non-convex losses.

Each block typically consists ofaggregation modules andfusion modules.

Our analysis further exposes that IGR encourages neurons with the same activation states for natural samples and the corresponding perturbed samples.

Mobility impairment caused by limb loss, aging, stroke, and other movement deficiencies isasignificant challenge facedbymillions ofindividualsworldwide. Advancedassistivetechnologies,suchasprosthesesandorthoses,havethepotential to greatly improve the quality of life for such individuals. A critical component in the design of these technologies is the accurate forecasting of reference joint motion forimpaired limbs,whichishindered bythescarcity ofjointlocomotion data available for these patients.

In this section, we first provide model parameters used for training the attack GANs. We then provide sample images from each cluster/class for each of the models, along with the generated noise using ourGAN models. In this section, we provide additional details for the defense approaches considered in this paper. B.1 RobustDeepClustering We provide hyperparameter values (Table 6) for training the GAN network for RUC, along with confusion matrices (Figures 37 - 39) and adversarial samples (Figures 40 - 42) obtained via our attack. Then, in Table 8 we provide the actual values used for generating the injection/detection bar plot figureinthemaintext.

Clustering models constitute aclass of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in moderndatascience.

Several recent papers considered a related question of a function's sensitivity to its various inputs [5,6,7].

Most recent research hasaddressed thisby focusing onlocal interpretability, which explains a model's individual predictions (e.g., the role of each feature in a patient's diagnosis) [25, 30, 34, 38]. Twospecial cases areS = andS = D, which respectively correspond to the mean prediction f (x ) = E[f(X)] and the full model predictionfD(x) = f(x).

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines.