For a machine learning model to be trusted/ used one would need to be confident in its capabilities of dealing with all possible scenarios. To that end, designing unit test cases for more complex and global problems could be costly and bordering on impossible to create.

**Idea**: We need a basic guideline that researchers and developers can adhere to when defining problems and outlining solutions, so that model interpretability can be defined accurately in terms of the problem statement.

**Solution**: This paper outlines the basics of machine learning interpretability, what that means for different users, and how to classify these into understandable categories that can be evaluated. This paper highlights the need for interpretability, which arises from *incompleteness*,either of the problem statement, or the problem domain knowledge. This paper provides three main categories to evaluating a model/ providing interpretations:
- *Application Grounded Evaluation*: These evaluations are more costly, and involve real humans evaluating real tasks that a model would take up. Domain knowledge is necessary for the humans evaluating the real task handled by the model.
- *Human Grounded Evaluation:* these evaluations are simpler than application grounded, as they simplify the complex task and have humans evaluate the simplified task. Domain knowledge is not necessary in such an evaluation. 
- *Functionally Grounded Evaluation:* No humans are involved in this version of evaluation, here previously evaluated models are perfected or tweaked to optimize certain functionality. Explanation quality is measured by a formal definition of interpretability.

This paper also outlines certain issues with the above three evaluation processes, there are certain questions that need answering before we can pick an evaluation method and metric. 
-To highlight the factors of interpretability, we are provided with the Data-driven approach. Here we analyze each task and the various methods used to fulfill the task and see which of these methods and tasks are most significant to the model.
- We are introduced to the term latent dimensions of interpretability, i.e. dimensions that are inferred not observed. These are divided into task related latent dimensions and method related latent dimensions, these are a long list of factors that are task specific or method specific.

Thus this paper provides a basic taxonomy for how we should evaluate our model, and how these evaluations differ from problem to problem.  The ideal scenario outlined is that researchers provide the relevant information to evaluate their proposition correctly (correctly in terms of the domain and the problem scope).

During the development of neural circuitry, neurons of different kinds establish specific synaptic connections by selecting appropriate targets from large numbers of alternatives. The range of alternative targets is reduced by well organised patterns of growth, termination, and branching that deliver the terminals of appropriate pre- and postsynaptic partners to restricted volumes of the developing nervous system. We use the axons of embryonic Drosophila sensory neurons as a model system in which to study the way in which growing neurons are guided to terminate in specific volumes of the developing nervous system. The mediolateral positions of sensory arbors are controlled by the response of Robo receptors to a Slit gradient. Here we make a genetic analysis of factors regulating position in the dorso-ventral axis. We find that dorso-ventral layers of neuropile contain different levels and combinations of Semaphorins. We demonstrate the existence of a central to dorsal and central to ventral gradient of Sema 2a, perpendicular to the Slit gradient. We show that a combination of Plexin A (Plex A) and Plexin B (Plex B) receptors specifies the ventral projection of sensory neurons by responding to high concentrations of Semaphorin 1a (Sema 1a) and Semaphorin 2a (Sema 2a). Together our findings support the idea that axons are delivered to particular regions of the neuropile by their responses to systems of positional cues in each dimension.

They created a really nice trick to optimize the $ {L}_{0} $ Pseudo Norm - Regularization on the sorted (By magnitude) values of the optimization variable.

Their code is available at - [The Trimmed Lasso: Sparsity and Robustness](https://github.com/copenhaver/trimmedlasso).

The paper explores the usefulness of eye tracking for the task of POS tagging. The assumption is that readers skip quickly over closed class words, and fixate longer on rare on ambiguous words.

The experiments are performed on unsupervised POS tagging – a second-order HMM uses constraints on possible tags for each word (based on a dictionary), but no explicit annotated data is required. They show that including the eye tracking features improves performance by quite a bit. Surprisingly, it seems to be better to average eye tracking features over all training tokens of the same type, as opposed to using using the data for each individual token, which means eye tracking is only used during the training stage.

Investigating a range of auxiliary objectives for training a sequence labeling system for error detection. Automatically generated dependency relations and POS tags perform surprisingly well as gold labels for multi-task learning. Learning different objectives at the same time works better than doing them in sequence or switching.

https://i.imgur.com/81PvMfj.png