The left side shows the effect of pruning of neurons in the weight-matrix of afully-connected layer. The rows in white correspond to neurons pruned in theassociated layer while thecolumns inwhite represent theeffectofremoving neurons from the previous layers. On the right, we allude to the possibility of collapsing the pruned matrix into a smaller,denseone.

Common modifications include:(i)tuning attack parameters (e.g., number ofsteps),(ii)replacing network components to simplify the attack (e.g., removing randomization or non-differentiable components), and(iii) replacing the loss function optimized by the attack.

While many techniques for detecting these attacks have been proposed, theyareeasily bypassed when theadversary hasfullknowledge of the detection mechanism and adapts the attack strategy accordingly. In this paper,we adopt anovel perspectiveand regard the omnipresence of adversarial perturbations asastrength rather thanaweakness.

This paper presents anewUNIfied pre-trainedLanguageModel (UNILM)that can be fine-tuned for both natural language understanding and generation tasks.

Recent progress in the development of voltage indicators [1-8] has brought us closer to a longstanding goal incellular neuroscience: imaging the full spatiotemporal voltageonadendritic tree. These recordings have the potential (pun not intended) to resolve fundamental questions about the computations performed by dendrites -- questions that have remained open for more than a century[9,10].

Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-totree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer'ssinusoidal positional encodings, allowing ustoinstead useanovel positional encoding scheme to represent node positions within trees.

GMED-editedexamplesremain similar to their unedited forms, but can yield increased loss in the upcoming model updates, thereby making thefuture replays more effectiveinovercoming catastrophic forgetting.

This scaling behavior coincides with the one reportedinTripuranenietal. (2020)usingGaussiancomplexity.

Acompact summary comparing thedifferent properties of the various approaches mentioned above can be found in Table 1.

Inmanycases,thenumber ofdimensions in the data is much larger than the number of actual data samples.