Becoming a deep learning researcher without a PhD, graph neural network(GNN), time series, recommender system with Kyle Kranen - The Data Scientist Show#028
Mar 3, 2022
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Exploring deep learning research topics with Kyle Kranen, a Deep Learning Software Engineer at Nvidia. Topics include Graph Neural Network (GNN), Temporal Fusion Transformer (TFT), time series, and other insights into his career journey.
Temporal Fusion Transformers use attention and recurrent frameworks for multi-horizon forecasts.
TFT outperforms ARIMA and XGBoost in non-stationary time series data adaptation.
TFT manages seasonality and features without explicit instructions, enhancing adaptability.
Recommender systems focus on user experience metrics beyond click prediction accuracy.
Graph-based models offer personalized recommendations by analyzing user interactions.
Continuous growth, specialized knowledge, and mentorship are key to success in deep learning research.
Deep dives
Temporal Fusion Transformers in Time Series Forecasting
Temporal Fusion Transformers (TFT) employ attention and recurrent encoder-decoder frameworks to predict multi-horizon forecasts, extending beyond the next time step. The model incorporates feature input selection based on initial state and static information to gate input features, enhancing interpretability. TFT uses attention to reduce interpretability across both feature and temporal axes, allowing for detailed insights into what influences predictions.
Comparison with Statistical Models like ARIMA and XGBoost
In comparisons with statistical models like ARIMA and machine learning models like XGBoost, TFT has shown superior performance on various data sets. TFT's structure enables better adaptation to non-stationary time series data compared to ARIMA. The multi-axis comparison in TFT leads to accurate predictions, outperforming XGBoost particularly when accuracy is critical, such as in forecasting applications.
Handling Seasonality and Feature Importance in TFT
Seasonality and features are managed in TFT without explicit feature instructions. Seasonal inputs can be fed directly, while known continuous values influence feature masks. TFT can learn seasonality from the input data, providing adaptability to changing trends. Feature importance is evaluated based on the derived masks, offering insights into the model's computations.
Customizing and Maintaining Efficiency of Recommender Systems
Efforts in recommender systems improvement involve customizing pre-processing workflows to leverage GPU acceleration tools like KudyF. Optimizing table operations order to manage table size expansion efficiently is crucial to maintain processing efficiency. Techniques using frameworks like PySpark enhance scalability and performance in handling table operations.
Importance of User Experience in Recommender Systems
Recommender systems often focus on metrics like click prediction accuracy but may miss out on user experience. Recent studies like Recklist propose evaluation metrics to consider user satisfaction and uniqueness of recommendations. Utilizing a sequential approach can improve user interactions by balancing exploration and exploitation.
Challenges and Considerations in Recommender Systems
The podcast delves into the challenges of assessing user interactions beyond accuracy metrics in recommender systems. Incorporating user feedback and experiences beyond clicking can enhance system performance. Graph-based models offer a unique perspective by representing users and items as nodes, presenting a more nuanced approach to recommendation algorithms.
Exploring Graph Models in Recommender Systems
Graph-based approaches in recommender systems provide insights into analyzing user sequences and unique interactions. By incorporating graph structures, models can offer more personalized and diverse recommendations. The podcast discusses leveraging graph models to capture user preferences and enhance the overall recommendation process.
Future of Deep Learning and Research
The episode explores the evolving landscape of deep learning research, highlighting the potential for trillion-parameter models and novel applications in real-world scenarios like video compression. The future of research in computational drug discovery and reinforcement learning presents exciting opportunities for advancements in diverse domains.
Professional Growth and Skill Development
The conversation emphasizes continuous growth and learning as key drivers for success in deep learning research. Developing depth in specific domains, like graph neural networks or reinforcement learning, alongside honing technical skills with frameworks like PyTorch and TensorFlow, can propel individuals towards impactful research and problem-solving.
Balancing Depth and Breadth in Skill Acquisition
Focusing on depth in a specific domain rather than breadth of tools is advised for optimal skill acquisition. Delving deep into one or two frameworks or problem-solving approaches enables individuals to gain expertise and contribute meaningfully to projects. The episode stresses the importance of depth-first learning and specialized knowledge in deep learning domains.
Setting and Achieving Personal Goals
Setting clear timelines and expressing goals aloud can greatly assist in achieving personal and professional milestones. Emphasizing continuous progress, exploring new domains like long-distance biking and culinary skills, and ensuring growth in technical areas like reinforcement learning and computational drug discovery can lead to holistic personal development and fulfillment.
The Value of Intellectual Conversations and Restorative Practices
Engaging in intellectual discussions and restorative practices like walks can provide mental clarity and novel insights. Prioritizing interactions with family and friends, nurturing intellectual discourse, and embracing diverse experiences contribute to personal well-being and intellectual growth. Cultivating intellectual curiosity and receptivity to new ideas fosters continuous learning and development.
Mentorship, Accountability, and Learning Mindset
The episode underscores the significance of mentorship, continuous learning, and accountability in professional growth. Mentors play a pivotal role in providing guidance and insights, while embracing accountability and setting ambitious yet achievable goals fuels personal advancement. Cultivating a learning mindset, seeking intellectual challenges, and staying open to feedback and growth opportunities are essential for continuous development.
Kyle Kranen is a Deep Learning Software Engineer at Nvidia. Researching, implementing, and optimizing state of the art distributed deep learning models, using mainly Pytorch and Tensorflow. He has a unique combination of skillset of both hardware and software engineering. We talked about Graph Neural Network (GNN), Temporal Fusion Transformer (TFT), time series, and other deep learning research topics and his career journey. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science.
