Hello Reddit! We reviewed state-of-the-art Adversarial Attacks as well as Defenses against them in our paper. We cover images, graphs and text domains.

I eagerly look forward to your comments!

Paper: https://arxiv.org/abs/1909.08072

Hello everyone I have a project for my AI machine learning course that will be based on city data, namely Vancouver. The below link are the data sets that our project can be based upon. We are free to use outside data sets but must relate it to our city.

https://opendata.vancouver.ca/explore

I would really appreciate any guidance or input. We’re having a difficult time coming up with ideas, the only ones we have come up with are predicting of house property, bike theft, general crime prediction all of which would combine features from other data sets.

Thank you for reading my post!

I am working with a database that is spread across 7 tables and for ML stuff I need to join them together. However, as some of these rows are not sampled as frequently as others, this leaves a lot of nulls for some features. I want to interpolate these values but I’m not sure the most efficient way to do so. In other words, let’s say I have feature X sampled every 10 ms and feature Y every 1 second, and a third feature Z sampled every 15 seconds. I could store it in the database, but I don’t know if allowing that kind of storage capacity is feasible for us. Alternatively, I could calculate it for each row when I get batches for training, but I’m afraid that will become a bottleneck depending on how fast the interpolation is. Is there some obvious way of interpolating this efficiently that I’m not thinking of that will allow me to save on memory space?

Hi everyone,

I have a project where I have to do a quick POC of speech recognition in noisy environment and multispeaker setting. However, I am having a hard time finding a pretrained model with language model rescoring (or any other decoding helper). Any repo or links are welcome!

For preliminary research work, should one submit their paper to an average conference (e.g. ACCV, IJCNN, WACV, BMVC) or to a workshop organised in conjunction with a highly reputed conference (e.g., ICML/NeurIPS/ICLR/AAAI workshop)? What are the pros and cons for each option?

Related links:

• What value do top conference workshop papers (say CVPR, ICCV) add for a PhD admission in Computer Vision at top universities? How does it compare to less prestigious conferences (WACV, BMVC)? What impact does non top publications have?

• How valuable is to have a workshop paper for graduate admission?

• Is the paper in the NIPS ML4H (machine learning for health) workshop considered a good publication? How do you compare the paper in the NIPS ML4H workshop with a regular paper in a normal conference in computer science?

• Should one send their paper to an average conference or to a workshop held in conjunction with a highly reputed conference?

I am writing a GNN from scratch, to demonstrate to myself that I understand all the required concepts.

I am a bit confused on whether neighborhood aggregation operations require gradients to be tracked through those operations like mean and max of neighbors embeddings. In my code where I perform these operations, currently I do them within a with torch.no_grad() block because if I don’t each epoch takes forever.

Here my code for those operations:

def neighborhood_aggregation(self, adj_lists, feat, agg_method): # adj_lists is a dict of neighbors for every node in graph # e.g. adj_list = {0:{1, 4, 5, 6}, 1: {2, 4, 5}, ...} # node 0 has neighbors 1, 4, 5, 6 with torch.no_grad(): # construct aggregated neighborhood embedding dim = list(feat.size()) n_nodes = dim[0] feat_dim = dim[1] aggregated_embed = torch.Tensor(n_nodes, feat_dim) # aggregated embeddings for all nodes in graph. embed_element_vec = torch.arange(feat_dim) # for node_id, neighbor_node_ids in adj_lists.items(): neighborhood_embedding = feat[list(neighbor_node_ids), :] if agg_method == 'mean': aggregated_neigborhood_embedding = torch.mean(neighborhood_embedding, 0) elif agg_method == 'pool': aggregated_neigborhood_embedding = torch.max(neighborhood_embedding, 0)[0] else: raise KeyError('Aggregator type {} not recognized.'.format(agg_method)) aggregated_embed[node_id, embed_element_vec] = aggregated_neigborhood_embedding return aggregated_embed 

Note: The above code works, and I am getting very good results with it. It’s just I am not sure if what I am doing is wrong. IF it is wrong I was thinking that I need a 3D tensor for the aggregated_embed tensor [n_nodes, n_neighbors, embed_dim] (which requires_grad=False) and perform the mean/max on that tensor which would track gradients.

Thanks for any help.

For people interested in the history of technology — here’s an eccentric essay arguing that the first examples of NLP happened in medieval times. Mystics studying the Kabbala devised “sacred rules” for combining letters to generate prophetic texts and, sometimes, to create golems.

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/natural-language-processing-dates-back-to-kabbalist-mystics

“While specific technologies have changed over time, the basic idea of treating language as a material that can be artificially manipulated by rule-based systems has been pursued by many people in many cultures and for many different reasons. These historical experiments reveal the promise and perils of attempting to simulate human language in non-human ways—and they hold lessons for today’s practitioners of cutting-edge NLP techniques.”