I am curious, as I’ve been thinking about this for a while. To me, it seems as though we seem to be making improvements, but there is not a ton left to solve within this sub field. I don’t claim to be an expert by any stretch, but through all of the advancements we have made, we are capable of object detection, classification, image captioning for the contents of the image, image generation, and as we are closing in on depth perception and improvements on the 3D space, I feel like we are finding new applications for the tools we already have.

Thoughts?

I would love for someone to step in, call me a simpleton and give me all the reasons I am wrong and all of the problems we have yet to address within this space. 🙂

New artificial intelligence company Cerebras Systems is unveiling the largest semiconductor chip ever built.

https://venturebeat.com/2019/08/19/cerebras-systems-unveils-a-record-1-2-trillion-transistor-chip-for-ai/

TLDR: Are there non-LDA algorithms for topic modeling that are performant or state-of-the-art?

I’m working for a company that has a corpus of 10k articles for which they’d like to have topics identified and extracted. The company has a specific clientele; therefore the articles are already quite focused and topical (i.e., an engineering company would probably only write articles about engineering or engineering-adjacent things). Essentially, I’m trying to mine articles for sub-topics within our area of expertise.

I’m aware of LDA/LDA2Vec for topic modeling. In our case, since all of the articles are already of the same umbrella topic, the “topics” found via LDA tend to have an incredible amount of overlap relevance and salience metrics tend to prioritize words that relate both to the umbrella topic and the subtopic (unhelpful), or that are extremely rare occurrences (useless) – this is after multiple passes of filtering out frequent, rare, and low-value words.

I guess I’m hoping for something that either draws inferences from semantic meaning or uses a more sophisticated “topic” definition than probabilistic co-occurrence.

Thanks!

While trying out reinforcement learning I built some custom ball & beam environments since I was already familiar with it from control theory labs. I built it as a first order system where the angle of the beam is under full control (did not want to spend time simulating a motor). So it can be baselined by using a simple PID controller.

https://github.com/simon-larsson/ballbeam-gym

There are currently environments for three objectives:

• Balancing – just keeping the ball on beam
• Setpoint – keeping the ball as close as possible to a setpoint
• Throw – throwing the ball as far as possible to the right

The environments have two different state spaces. The agent can either use key-variables (position, velocity, angle) or the images from the visualization as state space.

Hope someone else wants to try it! 🙂

https://medium.com/@lessw/new-state-of-the-art-ai-optimizer-rectified-adam-radam-5d854730807b

This blog post discusses a new optimizer built on top of Adam, introduced in this paper by Liyuan Liu et al.. Essentially, they seek to understand why a warmup phase is beneficial for scheduling learning rates, and then identify the underlying problem to be related to high variance and poor generalization during the first few batches. They find that the issue can be remedied by using either a warmup/low initial learning rate, or by turning off momentum for the first couple of batches. As more training examples are fed in, the variance stabilizes and the learning rate/momentum can be increased. They therefore proposed a Rectified Adam optimizer that dynamically changes the momentum in a way that hedges against high variance. The author of the blog post tests an implementation in Fastai and finds that RAdam works well in many different contexts, enough to take the leaderboard of the Imagenette mini-competition.

Implementations can be found on the author’s Github.

Hi guys,

I’m working on a dataset and having some problems. I hope you can give me your insight.

So my objective is to predict customer churn based on incidents. Each incident is related to a contract which is related to a client. I need to predict the termination of the contract. The features can be grouped in 3 categories:

Client: client’s ID and some basic information about them

Contract: contract’s ID with their specific information and the target ‘In service/Terminated’

Incidents: every entry is an incident related to a contract with information like number of calls, date of creation, last change, incident category

Some clients have up to 10 contracts, some contracts have up to 20 incidents.

What I did is create a fresh table with the contracts only (and client’s information) and I now have to add relevant information for every contract.

I couldn’t help but find myself cherry picking some ‘relevant’ information like: Total incidents for the contract, total calls, last incident’s full information and also higher-level features like: number of contracts the user has, how much are terminated, total incidents for the user.

I feel it’s getting very messy and I’m still losing A LOT of information by doing this. Is it the only approach I have?

This was supposed to be a machine learning problem but seriously there’s nothing about machine learning at all, it’s pure data science.

Hi all, profuse apologies in advance if this is not the correct place to ask this question. I’ve attempted to look around for information (both online and offline), but perhaps I’m not hitting the right keywords, so I though I’d give this a try.

My question is specifically about “quant researcher” type careers, and what the pros/cons and other considerations are when taking up a job like that.

My understanding is that post-PhD (in ML, or whatever the department is that accommodated your ML research for the bulk of the PhD), the majority of people aim for (1) “research scientist” roles in industry, or (2) focus more on an academic career, or (3) both, simultaneously. Obviously this is a generalization, and there are many more things you can do with any STEM PhD for that matter, but these options seem to be the goals of many people.

What about (4) quant jobs in finance, such as in small/large trading / hedge funds / asset management / etc.? They frequently appear to offer extremely attractive packages, and require no experience in finance. However, for the most part the community seems to be rather separate from the (1) through (3) crowd I mentioned above, so I am unable to get a coherent picture of why some folks choose one path versus another, and the various things you should consider (e.g. long-term career trajectory, exit options, etc.) when taking your first job / internship in finance during / after your PhD.

Apologies for the naive question, and apologies again if this is not the right place to ask this kind of thing. Thank you in advance for your kind advice!

paper: https://arxiv.org/pdf/1903.10873.pdf

code: https://github.com/apchenstu/Facial_Details_Synthesis

Hi there,

I recently built a new PC with an AMD RX5700 (with AMD Ryzen 3600 CPU) and was wondering if there are any popular frameworks for training on the GPU, preferably in Python. I cannot seem to find a concise answer on this. Any help/suggestions would be greatly appreciated. Thank you.